---
_id: '45780'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
citation:
ama: 'Tornede A. Advanced Algorithm Selection with Machine Learning: Handling
Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.;
2023. doi:10.17619/UNIPB/1-1780
'
apa: 'Tornede, A. (2023). Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions. https://doi.org/10.17619/UNIPB/1-1780
'
bibtex: '@book{Tornede_2023, title={Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions}, DOI={10.17619/UNIPB/1-1780
}, author={Tornede, Alexander}, year={2023} }'
chicago: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions, 2023. https://doi.org/10.17619/UNIPB/1-1780
.'
ieee: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling
Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions.
2023.'
mla: 'Tornede, Alexander. Advanced Algorithm Selection with Machine Learning:
Handling Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta
Level Decisions. 2023, doi:10.17619/UNIPB/1-1780 .'
short: 'A. Tornede, Advanced Algorithm Selection with Machine Learning: Handling
Large Algorithm Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions,
2023.'
date_created: 2023-06-27T05:20:14Z
date_updated: 2023-08-04T06:01:49Z
ddc:
- '006'
department:
- _id: '355'
doi: '10.17619/UNIPB/1-1780 '
file:
- access_level: open_access
content_type: application/pdf
creator: ahetzer
date_created: 2023-07-24T08:40:35Z
date_updated: 2023-07-24T08:42:01Z
file_id: '46118'
file_name: dissertation_alexander_tornede_final_publishing_compressed.pdf
file_size: 4300633
relation: main_file
title: ' Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm
Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions'
file_date_updated: 2023-07-24T08:42:01Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '10'
grant_number: '160364472'
name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
title: 'Advanced Algorithm Selection with Machine Learning: Handling Large Algorithm
Sets, Learning From Censored Data, and Simplyfing Meta Level Decisions'
type: dissertation
user_id: '15504'
year: '2023'
...
---
_id: '21600'
abstract:
- lang: eng
text: Many problems in science and engineering require an efficient numerical approximation
of integrals or solutions to differential equations. For systems with rapidly
changing dynamics, an equidistant discretization is often inadvisable as it results
in prohibitively large errors or computational effort. To this end, adaptive schemes,
such as solvers based on Runge–Kutta pairs, have been developed which adapt the
step size based on local error estimations at each step. While the classical schemes
apply very generally and are highly efficient on regular systems, they can behave
suboptimally when an inefficient step rejection mechanism is triggered by structurally
complex systems such as chaotic systems. To overcome these issues, we propose
a method to tailor numerical schemes to the problem class at hand. This is achieved
by combining simple, classical quadrature rules or ODE solvers with data-driven
time-stepping controllers. Compared with learning solution operators to ODEs directly,
it generalizes better to unseen initial data as our approach employs classical
numerical schemes as base methods. At the same time it can make use of identified
structures of a problem class and, therefore, outperforms state-of-the-art adaptive
schemes. Several examples demonstrate superior efficiency. Source code is available
at https://github.com/lueckem/quadrature-ML.
author:
- first_name: Michael
full_name: Dellnitz, Michael
last_name: Dellnitz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marvin
full_name: Lücke, Marvin
last_name: Lücke
- first_name: Sina
full_name: Ober-Blöbaum, Sina
id: '16494'
last_name: Ober-Blöbaum
- first_name: Christian
full_name: Offen, Christian
id: '85279'
last_name: Offen
orcid: 0000-0002-5940-8057
- first_name: Sebastian
full_name: Peitz, Sebastian
id: '47427'
last_name: Peitz
orcid: 0000-0002-3389-793X
- first_name: Karlson
full_name: Pfannschmidt, Karlson
id: '13472'
last_name: Pfannschmidt
orcid: 0000-0001-9407-7903
citation:
ama: Dellnitz M, Hüllermeier E, Lücke M, et al. Efficient time stepping for numerical
integration using reinforcement learning. SIAM Journal on Scientific Computing.
2023;45(2):A579-A595. doi:10.1137/21M1412682
apa: Dellnitz, M., Hüllermeier, E., Lücke, M., Ober-Blöbaum, S., Offen, C., Peitz,
S., & Pfannschmidt, K. (2023). Efficient time stepping for numerical integration
using reinforcement learning. SIAM Journal on Scientific Computing, 45(2),
A579–A595. https://doi.org/10.1137/21M1412682
bibtex: '@article{Dellnitz_Hüllermeier_Lücke_Ober-Blöbaum_Offen_Peitz_Pfannschmidt_2023,
title={Efficient time stepping for numerical integration using reinforcement
learning}, volume={45}, DOI={10.1137/21M1412682},
number={2}, journal={SIAM Journal on Scientific Computing}, author={Dellnitz,
Michael and Hüllermeier, Eyke and Lücke, Marvin and Ober-Blöbaum, Sina and Offen,
Christian and Peitz, Sebastian and Pfannschmidt, Karlson}, year={2023}, pages={A579–A595}
}'
chicago: 'Dellnitz, Michael, Eyke Hüllermeier, Marvin Lücke, Sina Ober-Blöbaum,
Christian Offen, Sebastian Peitz, and Karlson Pfannschmidt. “Efficient Time Stepping
for Numerical Integration Using Reinforcement Learning.” SIAM Journal on Scientific
Computing 45, no. 2 (2023): A579–95. https://doi.org/10.1137/21M1412682.'
ieee: 'M. Dellnitz et al., “Efficient time stepping for numerical integration
using reinforcement learning,” SIAM Journal on Scientific Computing, vol.
45, no. 2, pp. A579–A595, 2023, doi: 10.1137/21M1412682.'
mla: Dellnitz, Michael, et al. “Efficient Time Stepping for Numerical Integration
Using Reinforcement Learning.” SIAM Journal on Scientific Computing, vol.
45, no. 2, 2023, pp. A579–95, doi:10.1137/21M1412682.
short: M. Dellnitz, E. Hüllermeier, M. Lücke, S. Ober-Blöbaum, C. Offen, S. Peitz,
K. Pfannschmidt, SIAM Journal on Scientific Computing 45 (2023) A579–A595.
date_created: 2021-04-09T07:59:19Z
date_updated: 2023-08-25T09:24:50Z
ddc:
- '510'
department:
- _id: '101'
- _id: '636'
- _id: '355'
- _id: '655'
doi: 10.1137/21M1412682
external_id:
arxiv:
- arXiv:2104.03562
has_accepted_license: '1'
intvolume: ' 45'
issue: '2'
language:
- iso: eng
main_file_link:
- url: https://epubs.siam.org/doi/reader/10.1137/21M1412682
page: A579-A595
publication: SIAM Journal on Scientific Computing
publication_status: published
related_material:
link:
- description: GitHub
relation: software
url: https://github.com/lueckem/quadrature-ML
status: public
title: Efficient time stepping for numerical integration using reinforcement learning
type: journal_article
user_id: '47427'
volume: 45
year: '2023'
...
---
_id: '24382'
author:
- first_name: Karina
full_name: Gevers, Karina
id: '83151'
last_name: Gevers
- first_name: Volker
full_name: Schöppner, Volker
id: '20530'
last_name: Schöppner
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Gevers K, Schöppner V, Hüllermeier E. Heated tool butt welding of two different
materials – Established methods versus artificial intelligence. In: ; 2021.'
apa: Gevers, K., Schöppner, V., & Hüllermeier, E. (2021). Heated tool butt
welding of two different materials – Established methods versus artificial intelligence.
International Institute of Welding, online.
bibtex: '@inproceedings{Gevers_Schöppner_Hüllermeier_2021, title={Heated tool butt
welding of two different materials – Established methods versus artificial intelligence},
author={Gevers, Karina and Schöppner, Volker and Hüllermeier, Eyke}, year={2021}
}'
chicago: Gevers, Karina, Volker Schöppner, and Eyke Hüllermeier. “Heated Tool Butt
Welding of Two Different Materials – Established Methods versus Artificial Intelligence,”
2021.
ieee: K. Gevers, V. Schöppner, and E. Hüllermeier, “Heated tool butt welding of
two different materials – Established methods versus artificial intelligence,”
presented at the International Institute of Welding, online, 2021.
mla: Gevers, Karina, et al. Heated Tool Butt Welding of Two Different Materials
– Established Methods versus Artificial Intelligence. 2021.
short: 'K. Gevers, V. Schöppner, E. Hüllermeier, in: 2021.'
conference:
end_date: 2021-07-14
location: online
name: International Institute of Welding
start_date: 2021-07-12
date_created: 2021-09-14T11:34:31Z
date_updated: 2022-01-06T06:56:19Z
department:
- _id: '367'
- _id: '355'
- _id: '321'
language:
- iso: eng
status: public
title: Heated tool butt welding of two different materials – Established methods
versus artificial intelligence
type: conference
user_id: '83151'
year: '2021'
...
---
_id: '21004'
abstract:
- lang: eng
text: 'Automated machine learning (AutoML) supports the algorithmic construction
and data-specific customization of machine learning pipelines, including the selection,
combination, and parametrization of machine learning algorithms as main constituents.
Generally speaking, AutoML approaches comprise two major components: a search
space model and an optimizer for traversing the space. Recent approaches have
shown impressive results in the realm of supervised learning, most notably (single-label)
classification (SLC). Moreover, first attempts at extending these approaches towards
multi-label classification (MLC) have been made. While the space of candidate
pipelines is already huge in SLC, the complexity of the search space is raised
to an even higher power in MLC. One may wonder, therefore, whether and to what
extent optimizers established for SLC can scale to this increased complexity,
and how they compare to each other. This paper makes the following contributions:
First, we survey existing approaches to AutoML for MLC. Second, we augment these
approaches with optimizers not previously tried for MLC. Third, we propose a benchmarking
framework that supports a fair and systematic comparison. Fourth, we conduct an
extensive experimental study, evaluating the methods on a suite of MLC problems.
We find a grammar-based best-first search to compare favorably to other optimizers.'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. AutoML for Multi-Label Classification:
Overview and Empirical Evaluation. IEEE Transactions on Pattern Analysis and
Machine Intelligence. Published online 2021:1-1. doi:10.1109/tpami.2021.3051276'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (2021). AutoML
for Multi-Label Classification: Overview and Empirical Evaluation. IEEE Transactions
on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2021.3051276'
bibtex: '@article{Wever_Tornede_Mohr_Hüllermeier_2021, title={AutoML for Multi-Label
Classification: Overview and Empirical Evaluation}, DOI={10.1109/tpami.2021.3051276},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, author={Wever,
Marcel Dominik and Tornede, Alexander and Mohr, Felix and Hüllermeier, Eyke},
year={2021}, pages={1–1} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“AutoML for Multi-Label Classification: Overview and Empirical Evaluation.” IEEE
Transactions on Pattern Analysis and Machine Intelligence, 2021, 1–1. https://doi.org/10.1109/tpami.2021.3051276.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “AutoML for Multi-Label
Classification: Overview and Empirical Evaluation,” IEEE Transactions on Pattern
Analysis and Machine Intelligence, pp. 1–1, 2021, doi: 10.1109/tpami.2021.3051276.'
mla: 'Wever, Marcel Dominik, et al. “AutoML for Multi-Label Classification: Overview
and Empirical Evaluation.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, 2021, pp. 1–1, doi:10.1109/tpami.2021.3051276.'
short: M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (2021) 1–1.
date_created: 2021-01-16T14:48:13Z
date_updated: 2022-01-06T06:54:42Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1109/tpami.2021.3051276
keyword:
- Automated Machine Learning
- Multi Label Classification
- Hierarchical Planning
- Bayesian Optimization
language:
- iso: eng
page: 1-1
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_identifier:
issn:
- 0162-8828
- 2160-9292
- 1939-3539
publication_status: published
status: public
title: 'AutoML for Multi-Label Classification: Overview and Empirical Evaluation'
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21092'
abstract:
- lang: eng
text: "Automated Machine Learning (AutoML) seeks to automatically find so-called
machine learning pipelines that maximize the prediction performance when being
used to train a model on a given dataset. One of the main and yet open challenges
in AutoML is an effective use of computational resources: An AutoML process involves
the evaluation of many candidate pipelines, which are costly but often ineffective
because they are canceled due to a timeout.\r\nIn this paper, we present an approach
to predict the runtime of two-step machine learning pipelines with up to one pre-processor,
which can be used to anticipate whether or not a pipeline will time out. Separate
runtime models are trained offline for each algorithm that may be used in a pipeline,
and an overall prediction is derived from these models. We empirically show that
the approach increases successful evaluations made by an AutoML tool while preserving
or even improving on the previously best solutions."
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Mohr F, Wever MD, Tornede A, Hüllermeier E. Predicting Machine Learning Pipeline
Runtimes in the Context of Automated Machine Learning. IEEE Transactions on
Pattern Analysis and Machine Intelligence.
apa: Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (n.d.). Predicting
Machine Learning Pipeline Runtimes in the Context of Automated Machine Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence.
bibtex: '@article{Mohr_Wever_Tornede_Hüllermeier, title={Predicting Machine Learning
Pipeline Runtimes in the Context of Automated Machine Learning}, journal={IEEE
Transactions on Pattern Analysis and Machine Intelligence}, publisher={IEEE},
author={Mohr, Felix and Wever, Marcel Dominik and Tornede, Alexander and Hüllermeier,
Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning.” IEEE Transactions on Pattern Analysis and Machine Intelligence,
n.d.
ieee: F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “Predicting Machine
Learning Pipeline Runtimes in the Context of Automated Machine Learning,” IEEE
Transactions on Pattern Analysis and Machine Intelligence.
mla: Mohr, Felix, et al. “Predicting Machine Learning Pipeline Runtimes in the Context
of Automated Machine Learning.” IEEE Transactions on Pattern Analysis and Machine
Intelligence, IEEE.
short: F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, IEEE Transactions on Pattern
Analysis and Machine Intelligence (n.d.).
date_created: 2021-01-27T13:45:52Z
date_updated: 2022-01-06T06:54:45Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: IEEE Transactions on Pattern Analysis and Machine Intelligence
publication_status: accepted
publisher: IEEE
status: public
title: Predicting Machine Learning Pipeline Runtimes in the Context of Automated Machine
Learning
type: journal_article
user_id: '5786'
year: '2021'
...
---
_id: '21535'
author:
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Róbert
full_name: Busa-Fekete, Róbert
last_name: Busa-Fekete
- first_name: Adil
full_name: El Mesaoudi-Paul, Adil
last_name: El Mesaoudi-Paul
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Bengs V, Busa-Fekete R, El Mesaoudi-Paul A, Hüllermeier E. Preference-based
Online Learning with Dueling Bandits: A Survey. Journal of Machine Learning
Research. 2021;22(7):1-108.'
apa: 'Bengs, V., Busa-Fekete, R., El Mesaoudi-Paul, A., & Hüllermeier, E. (2021).
Preference-based Online Learning with Dueling Bandits: A Survey. Journal of
Machine Learning Research, 22(7), 1–108.'
bibtex: '@article{Bengs_Busa-Fekete_El Mesaoudi-Paul_Hüllermeier_2021, title={Preference-based
Online Learning with Dueling Bandits: A Survey}, volume={22}, number={7}, journal={Journal
of Machine Learning Research}, author={Bengs, Viktor and Busa-Fekete, Róbert and
El Mesaoudi-Paul, Adil and Hüllermeier, Eyke}, year={2021}, pages={1–108} }'
chicago: 'Bengs, Viktor, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, and Eyke Hüllermeier.
“Preference-Based Online Learning with Dueling Bandits: A Survey.” Journal
of Machine Learning Research 22, no. 7 (2021): 1–108.'
ieee: 'V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, and E. Hüllermeier, “Preference-based
Online Learning with Dueling Bandits: A Survey,” Journal of Machine Learning
Research, vol. 22, no. 7, pp. 1–108, 2021.'
mla: 'Bengs, Viktor, et al. “Preference-Based Online Learning with Dueling Bandits:
A Survey.” Journal of Machine Learning Research, vol. 22, no. 7, 2021,
pp. 1–108.'
short: V. Bengs, R. Busa-Fekete, A. El Mesaoudi-Paul, E. Hüllermeier, Journal of
Machine Learning Research 22 (2021) 1–108.
date_created: 2021-03-18T11:15:38Z
date_updated: 2022-01-06T06:55:03Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
intvolume: ' 22'
issue: '7'
language:
- iso: eng
page: 1-108
publication: Journal of Machine Learning Research
quality_controlled: '1'
status: public
title: 'Preference-based Online Learning with Dueling Bandits: A Survey'
type: journal_article
user_id: '76599'
volume: 22
year: '2021'
...
---
_id: '21570'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Hüllermeier E. Coevolution of Remaining Useful
Lifetime Estimation Pipelines for Automated Predictive Maintenance. In: Proceedings
of the Genetic and Evolutionary Computation Conference. ; 2021.'
apa: Tornede, T., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021). Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance.
Proceedings of the Genetic and Evolutionary Computation Conference. Genetic
and Evolutionary Computation Conference.
bibtex: '@inproceedings{Tornede_Tornede_Wever_Hüllermeier_2021, title={Coevolution
of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
author={Tornede, Tanja and Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier,
Eyke}, year={2021} }'
chicago: Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, and Eyke Hüllermeier.
“Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated Predictive
Maintenance.” In Proceedings of the Genetic and Evolutionary Computation Conference,
2021.
ieee: T. Tornede, A. Tornede, M. D. Wever, and E. Hüllermeier, “Coevolution of Remaining
Useful Lifetime Estimation Pipelines for Automated Predictive Maintenance,” presented
at the Genetic and Evolutionary Computation Conference, 2021.
mla: Tornede, Tanja, et al. “Coevolution of Remaining Useful Lifetime Estimation
Pipelines for Automated Predictive Maintenance.” Proceedings of the Genetic
and Evolutionary Computation Conference, 2021.
short: 'T. Tornede, A. Tornede, M.D. Wever, E. Hüllermeier, in: Proceedings of the
Genetic and Evolutionary Computation Conference, 2021.'
conference:
end_date: 2021-07-14
name: Genetic and Evolutionary Computation Conference
start_date: 2021-07-10
date_created: 2021-03-26T09:14:19Z
date_updated: 2022-01-06T06:55:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the Genetic and Evolutionary Computation Conference
status: public
title: Coevolution of Remaining Useful Lifetime Estimation Pipelines for Automated
Predictive Maintenance
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '23779'
abstract:
- lang: ger
text: "Produktentstehung (PE) bezieht sich auf den Prozess der Planung und Entwicklung
eines Produkts sowie der damit verbundenen Dienstleistungen von der ersten Idee
bis zur Herstellung und zum Vertrieb. Während dieses Prozesses gibt es zahlreiche
Aufgaben, die von menschlichem Fachwissen abhängen und typischerweise von erfahrenen
Experten übernommen werden. Da sich das Feld der Künstlichen Intelligenz (KI)
immer weiterentwickelt und seinen Weg in den Fertigungssektor findet, gibt es
viele Möglichkeiten für eine Anwendung von KI, um bei der Lösung der oben genannten
Aufgaben zu helfen. In diesem Paper geben wir einen umfassenden Überblick über
den aktuellen Stand der Technik des Einsatzes von KI in der PE. \r\nIm Detail
analysieren wir 40 bestehende Surveys zu KI in der PE und 94 Case Studies, um
herauszufinden, welche Bereiche der PE von der aktuellen Forschung in diesem Bereich
vorrangig adressiert werden, wie ausgereift die diskutierten KI-Methoden sind
und inwieweit datenzentrierte Ansätze in der aktuellen Forschung genutzt werden."
- lang: eng
text: "Product Creation (PC) refers to the process of planning and developing a
product as well as related services from the initial idea until manufacturing
and distribution. Throughout this process, there are numerous tasks that depend
on human expertise and are typically undertaken by experienced practitioners.
As the field of Artificial Intelligence (AI) continues to evolve and finds its
way into the manufacturing sector, there exist many possibilities for an application
of AI in order to assist in solving aforementioned tasks. In this work, we provide
a comprehensive overview of the current state of the art of the use of AI in PC.
\r\nIn detail, we analyze 40 existing surveys on AI in PC and 94 case studies
in order to find out which areas of PC are primarily addressed by current research
in this field, how mature the discussed AI methods are, and to which extent data-centric
approaches are utilized in current research."
author:
- first_name: Ruslan
full_name: Bernijazov, Ruslan
last_name: Bernijazov
- first_name: Alexander
full_name: Dicks, Alexander
last_name: Dicks
- first_name: Roman
full_name: Dumitrescu, Roman
id: '16190'
last_name: Dumitrescu
- first_name: Marc
full_name: Foullois, Marc
last_name: Foullois
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Gökce
full_name: Karakaya, Gökce
last_name: Karakaya
- first_name: Patrick
full_name: Ködding, Patrick
id: '45402'
last_name: Ködding
- first_name: Volker
full_name: Lohweg, Volker
last_name: Lohweg
- first_name: Manuel
full_name: Malatyali, Manuel
id: '41265'
last_name: Malatyali
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
id: '15523'
last_name: Meyer auf der Heide
- first_name: Melina
full_name: Panzner, Melina
last_name: Panzner
- first_name: Christian
full_name: Soltenborn, Christian
id: '1737'
last_name: Soltenborn
orcid: 0000-0002-0342-8227
citation:
ama: 'Bernijazov R, Dicks A, Dumitrescu R, et al. A Meta-Review on Artificial Intelligence
in Product Creation. In: Proceedings of the 30th International Joint Conference
on Artificial Intelligence (IJCAI-21). ; 2021.'
apa: Bernijazov, R., Dicks, A., Dumitrescu, R., Foullois, M., Hanselle, J. M., Hüllermeier,
E., Karakaya, G., Ködding, P., Lohweg, V., Malatyali, M., Meyer auf der Heide,
F., Panzner, M., & Soltenborn, C. (2021). A Meta-Review on Artificial Intelligence
in Product Creation. Proceedings of the 30th International Joint Conference
on Artificial Intelligence (IJCAI-21). 30th International Joint Conference
on Artificial Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal,
Kanada.
bibtex: '@inproceedings{Bernijazov_Dicks_Dumitrescu_Foullois_Hanselle_Hüllermeier_Karakaya_Ködding_Lohweg_Malatyali_et
al._2021, title={A Meta-Review on Artificial Intelligence in Product Creation},
booktitle={Proceedings of the 30th International Joint Conference on Artificial
Intelligence (IJCAI-21)}, author={Bernijazov, Ruslan and Dicks, Alexander and
Dumitrescu, Roman and Foullois, Marc and Hanselle, Jonas Manuel and Hüllermeier,
Eyke and Karakaya, Gökce and Ködding, Patrick and Lohweg, Volker and Malatyali,
Manuel and et al.}, year={2021} }'
chicago: Bernijazov, Ruslan, Alexander Dicks, Roman Dumitrescu, Marc Foullois, Jonas
Manuel Hanselle, Eyke Hüllermeier, Gökce Karakaya, et al. “A Meta-Review on Artificial
Intelligence in Product Creation.” In Proceedings of the 30th International
Joint Conference on Artificial Intelligence (IJCAI-21), 2021.
ieee: R. Bernijazov et al., “A Meta-Review on Artificial Intelligence in Product
Creation,” presented at the 30th International Joint Conference on Artificial
Intelligence (IJCAI 2021) - Workshop “AI and Product Design,” Montreal, Kanada,
2021.
mla: Bernijazov, Ruslan, et al. “A Meta-Review on Artificial Intelligence in Product
Creation.” Proceedings of the 30th International Joint Conference on Artificial
Intelligence (IJCAI-21), 2021.
short: 'R. Bernijazov, A. Dicks, R. Dumitrescu, M. Foullois, J.M. Hanselle, E. Hüllermeier,
G. Karakaya, P. Ködding, V. Lohweg, M. Malatyali, F. Meyer auf der Heide, M. Panzner,
C. Soltenborn, in: Proceedings of the 30th International Joint Conference on Artificial
Intelligence (IJCAI-21), 2021.'
conference:
end_date: 2021-08-26
location: Montreal, Kanada
name: 30th International Joint Conference on Artificial Intelligence (IJCAI 2021)
- Workshop "AI and Product Design"
start_date: 2021-08-19
date_created: 2021-09-06T08:23:45Z
date_updated: 2022-01-06T06:55:59Z
department:
- _id: '63'
- _id: '563'
- _id: '355'
- _id: '241'
keyword:
- Artificial Intelligence Product Creation Literature Review
language:
- iso: eng
main_file_link:
- url: https://www.hsu-hh.de/imb/wp-content/uploads/sites/677/2021/08/A-Meta-Review-on-Artificial-Intelligence-in-Product-Creation.pdf
publication: Proceedings of the 30th International Joint Conference on Artificial
Intelligence (IJCAI-21)
publication_status: epub_ahead
quality_controlled: '1'
status: public
title: A Meta-Review on Artificial Intelligence in Product Creation
type: conference
user_id: '15415'
year: '2021'
...
---
_id: '22913'
author:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hüllermeier E, Mohr F, Tornede A, Wever MD. Automated Machine Learning, Bounded
Rationality, and Rational Metareasoning. In: ; 2021.'
apa: Hüllermeier, E., Mohr, F., Tornede, A., & Wever, M. D. (2021). Automated
Machine Learning, Bounded Rationality, and Rational Metareasoning. ECML/PKDD
Workshop on Automating Data Science, Bilbao (Virtual).
bibtex: '@inproceedings{Hüllermeier_Mohr_Tornede_Wever_2021, title={Automated Machine
Learning, Bounded Rationality, and Rational Metareasoning}, author={Hüllermeier,
Eyke and Mohr, Felix and Tornede, Alexander and Wever, Marcel Dominik}, year={2021}
}'
chicago: Hüllermeier, Eyke, Felix Mohr, Alexander Tornede, and Marcel Dominik Wever.
“Automated Machine Learning, Bounded Rationality, and Rational Metareasoning,”
2021.
ieee: E. Hüllermeier, F. Mohr, A. Tornede, and M. D. Wever, “Automated Machine Learning,
Bounded Rationality, and Rational Metareasoning,” presented at the ECML/PKDD Workshop
on Automating Data Science, Bilbao (Virtual), 2021.
mla: Hüllermeier, Eyke, et al. Automated Machine Learning, Bounded Rationality,
and Rational Metareasoning. 2021.
short: 'E. Hüllermeier, F. Mohr, A. Tornede, M.D. Wever, in: 2021.'
conference:
end_date: 2021-09-17
location: Bilbao (Virtual)
name: ECML/PKDD Workshop on Automating Data Science
start_date: 2021-09-13
date_created: 2021-08-02T07:46:29Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
quality_controlled: '1'
status: public
title: Automated Machine Learning, Bounded Rationality, and Rational Metareasoning
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '22914'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Mohr F, Wever MD. Replacing the Ex-Def Baseline in AutoML by Naive AutoML.
In: ; 2021.'
apa: Mohr, F., & Wever, M. D. (2021). Replacing the Ex-Def Baseline in AutoML
by Naive AutoML. 8th ICML Workshop on Automated Machine Learning, Virtual.
bibtex: '@inproceedings{Mohr_Wever_2021, title={Replacing the Ex-Def Baseline in
AutoML by Naive AutoML}, author={Mohr, Felix and Wever, Marcel Dominik}, year={2021}
}'
chicago: Mohr, Felix, and Marcel Dominik Wever. “Replacing the Ex-Def Baseline in
AutoML by Naive AutoML,” 2021.
ieee: F. Mohr and M. D. Wever, “Replacing the Ex-Def Baseline in AutoML by Naive
AutoML,” presented at the 8th ICML Workshop on Automated Machine Learning, Virtual,
2021.
mla: Mohr, Felix, and Marcel Dominik Wever. Replacing the Ex-Def Baseline in
AutoML by Naive AutoML. 2021.
short: 'F. Mohr, M.D. Wever, in: 2021.'
conference:
end_date: 2021-07-23
location: Virtual
name: 8th ICML Workshop on Automated Machine Learning
start_date: 2021-07-23
date_created: 2021-08-02T07:48:07Z
date_updated: 2022-01-06T06:55:43Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
status: public
title: Replacing the Ex-Def Baseline in AutoML by Naive AutoML
type: conference
user_id: '5786'
year: '2021'
...
---
_id: '27381'
abstract:
- lang: eng
text: Graph neural networks (GNNs) have been successfully applied in many structured
data domains, with applications ranging from molecular property prediction to
the analysis of social networks. Motivated by the broad applicability of GNNs,
we propose the family of so-called RankGNNs, a combination of neural Learning
to Rank (LtR) methods and GNNs. RankGNNs are trained with a set of pair-wise preferences
between graphs, suggesting that one of them is preferred over the other. One practical
application of this problem is drug screening, where an expert wants to find the
most promising molecules in a large collection of drug candidates. We empirically
demonstrate that our proposed pair-wise RankGNN approach either significantly
outperforms or at least matches the ranking performance of the naive point-wise
baseline approach, in which the LtR problem is solved via GNN-based graph regression.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Hüllermeier E. Ranking Structured Objects with Graph Neural Networks.
In: Soares C, Torgo L, eds. Proceedings of The 24th International Conference
on Discovery Science (DS 2021). Vol 12986. Lecture Notes in Computer Science.
Springer; 2021:166-180. doi:10.1007/978-3-030-88942-5'
apa: Damke, C., & Hüllermeier, E. (2021). Ranking Structured Objects with Graph
Neural Networks. In C. Soares & L. Torgo (Eds.), Proceedings of The 24th
International Conference on Discovery Science (DS 2021) (Vol. 12986, pp. 166–180).
Springer. https://doi.org/10.1007/978-3-030-88942-5
bibtex: '@inproceedings{Damke_Hüllermeier_2021, series={Lecture Notes in Computer
Science}, title={Ranking Structured Objects with Graph Neural Networks}, volume={12986},
DOI={10.1007/978-3-030-88942-5},
booktitle={Proceedings of The 24th International Conference on Discovery Science
(DS 2021)}, publisher={Springer}, author={Damke, Clemens and Hüllermeier, Eyke},
editor={Soares, Carlos and Torgo, Luis}, year={2021}, pages={166–180}, collection={Lecture
Notes in Computer Science} }'
chicago: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with
Graph Neural Networks.” In Proceedings of The 24th International Conference
on Discovery Science (DS 2021), edited by Carlos Soares and Luis Torgo, 12986:166–80.
Lecture Notes in Computer Science. Springer, 2021. https://doi.org/10.1007/978-3-030-88942-5.
ieee: 'C. Damke and E. Hüllermeier, “Ranking Structured Objects with Graph Neural
Networks,” in Proceedings of The 24th International Conference on Discovery
Science (DS 2021), Halifax, Canada, 2021, vol. 12986, pp. 166–180, doi: 10.1007/978-3-030-88942-5.'
mla: Damke, Clemens, and Eyke Hüllermeier. “Ranking Structured Objects with Graph
Neural Networks.” Proceedings of The 24th International Conference on Discovery
Science (DS 2021), edited by Carlos Soares and Luis Torgo, vol. 12986, Springer,
2021, pp. 166–80, doi:10.1007/978-3-030-88942-5.
short: 'C. Damke, E. Hüllermeier, in: C. Soares, L. Torgo (Eds.), Proceedings of
The 24th International Conference on Discovery Science (DS 2021), Springer, 2021,
pp. 166–180.'
conference:
end_date: 2021-10-13
location: Halifax, Canada
name: 24th International Conference on Discovery Science
start_date: 2021-10-11
date_created: 2021-11-11T14:15:18Z
date_updated: 2022-04-11T22:08:12Z
department:
- _id: '355'
doi: 10.1007/978-3-030-88942-5
editor:
- first_name: Carlos
full_name: Soares, Carlos
last_name: Soares
- first_name: Luis
full_name: Torgo, Luis
last_name: Torgo
external_id:
arxiv:
- '2104.08869'
intvolume: ' 12986'
keyword:
- Graph-structured data
- Graph neural networks
- Preference learning
- Learning to rank
language:
- iso: eng
page: 166-180
publication: Proceedings of The 24th International Conference on Discovery Science
(DS 2021)
publication_identifier:
isbn:
- '9783030889418'
- '9783030889425'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
quality_controlled: '1'
series_title: Lecture Notes in Computer Science
status: public
title: Ranking Structured Objects with Graph Neural Networks
type: conference
user_id: '48192'
volume: 12986
year: '2021'
...
---
_id: '27284'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: Wever MD. Automated Machine Learning for Multi-Label Classification.;
2021. doi:10.17619/UNIPB/1-1302
apa: Wever, M. D. (2021). Automated Machine Learning for Multi-Label Classification.
https://doi.org/10.17619/UNIPB/1-1302
bibtex: '@book{Wever_2021, title={Automated Machine Learning for Multi-Label Classification},
DOI={10.17619/UNIPB/1-1302},
author={Wever, Marcel Dominik}, year={2021} }'
chicago: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification,
2021. https://doi.org/10.17619/UNIPB/1-1302.
ieee: M. D. Wever, Automated Machine Learning for Multi-Label Classification.
2021.
mla: Wever, Marcel Dominik. Automated Machine Learning for Multi-Label Classification.
2021, doi:10.17619/UNIPB/1-1302.
short: M.D. Wever, Automated Machine Learning for Multi-Label Classification, 2021.
date_created: 2021-11-08T14:05:19Z
date_updated: 2022-04-13T09:39:56Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.17619/UNIPB/1-1302
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2022-04-13T09:35:25Z
date_updated: 2022-04-13T09:39:56Z
file_id: '30886'
file_name: dissertation_publish_upload.pdf
file_size: 8098177
relation: main_file
file_date_updated: 2022-04-13T09:39:56Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication_status: published
status: public
supervisor:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
title: Automated Machine Learning for Multi-Label Classification
type: dissertation
user_id: '33176'
year: '2021'
...
---
_id: '21198'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Superset
Learning: Constructing Algorithm Selectors from Imprecise Performance Data. Published
online 2021.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2021).
Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data. The 25th Pacific-Asia Conference on Knowledge
Discovery and Data Mining (PAKDD-2021), Delhi, India.'
bibtex: '@article{Hanselle_Tornede_Wever_Hüllermeier_2021, series={PAKDD}, title={Algorithm
Selection as Superset Learning: Constructing Algorithm Selectors from Imprecise
Performance Data}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2021}, collection={PAKDD} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Algorithm Selection as Superset Learning: Constructing Algorithm
Selectors from Imprecise Performance Data.” PAKDD, 2021.'
ieee: 'J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection
as Superset Learning: Constructing Algorithm Selectors from Imprecise Performance
Data.” 2021.'
mla: 'Hanselle, Jonas Manuel, et al. Algorithm Selection as Superset Learning:
Constructing Algorithm Selectors from Imprecise Performance Data. 2021.'
short: J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, (2021).
conference:
end_date: 2021-05-14
location: Delhi, India
name: The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021)
start_date: 2021-05-11
date_created: 2021-02-09T09:30:14Z
date_updated: 2022-08-24T12:49:06Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
series_title: PAKDD
status: public
title: 'Algorithm Selection as Superset Learning: Constructing Algorithm Selectors
from Imprecise Performance Data'
type: conference
user_id: '38209'
year: '2021'
...
---
_id: '19521'
author:
- first_name: Karlson
full_name: Pfannschmidt, Karlson
last_name: Pfannschmidt
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Pfannschmidt K, Hüllermeier E. Learning Choice Functions via Pareto-Embeddings.
In: Lecture Notes in Computer Science. Cham; 2020. doi:10.1007/978-3-030-58285-2_30'
apa: Pfannschmidt, K., & Hüllermeier, E. (2020). Learning Choice Functions via
Pareto-Embeddings. In Lecture Notes in Computer Science. Cham. https://doi.org/10.1007/978-3-030-58285-2_30
bibtex: '@inbook{Pfannschmidt_Hüllermeier_2020, place={Cham}, title={Learning Choice
Functions via Pareto-Embeddings}, DOI={10.1007/978-3-030-58285-2_30},
booktitle={Lecture Notes in Computer Science}, author={Pfannschmidt, Karlson and
Hüllermeier, Eyke}, year={2020} }'
chicago: Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions
via Pareto-Embeddings.” In Lecture Notes in Computer Science. Cham, 2020.
https://doi.org/10.1007/978-3-030-58285-2_30.
ieee: K. Pfannschmidt and E. Hüllermeier, “Learning Choice Functions via Pareto-Embeddings,”
in Lecture Notes in Computer Science, Cham, 2020.
mla: Pfannschmidt, Karlson, and Eyke Hüllermeier. “Learning Choice Functions via
Pareto-Embeddings.” Lecture Notes in Computer Science, 2020, doi:10.1007/978-3-030-58285-2_30.
short: 'K. Pfannschmidt, E. Hüllermeier, in: Lecture Notes in Computer Science,
Cham, 2020.'
date_created: 2020-09-17T10:52:41Z
date_updated: 2022-01-06T06:54:06Z
department:
- _id: '7'
- _id: '355'
doi: 10.1007/978-3-030-58285-2_30
language:
- iso: eng
place: Cham
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Lecture Notes in Computer Science
publication_identifier:
isbn:
- '9783030582845'
- '9783030582852'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Learning Choice Functions via Pareto-Embeddings
type: book_chapter
user_id: '13472'
year: '2020'
...
---
_id: '19953'
abstract:
- lang: eng
text: Current GNN architectures use a vertex neighborhood aggregation scheme, which
limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman
(WL) graph isomorphism test. Here, we propose a novel graph convolution operator
that is based on the 2-dimensional WL test. We formally show that the resulting
2-WL-GNN architecture is more discriminative than existing GNN approaches. This
theoretical result is complemented by experimental studies using synthetic and
real data. On multiple common graph classification benchmarks, we demonstrate
that the proposed model is competitive with state-of-the-art graph kernels and
GNNs.
author:
- first_name: Clemens
full_name: Damke, Clemens
id: '48192'
last_name: Damke
orcid: 0000-0002-0455-0048
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Damke C, Melnikov V, Hüllermeier E. A Novel Higher-order Weisfeiler-Lehman
Graph Convolution. In: Jialin Pan S, Sugiyama M, eds. Proceedings of the 12th
Asian Conference on Machine Learning (ACML 2020). Vol 129. Proceedings of
Machine Learning Research. Bangkok, Thailand: PMLR; 2020:49-64.'
apa: 'Damke, C., Melnikov, V., & Hüllermeier, E. (2020). A Novel Higher-order
Weisfeiler-Lehman Graph Convolution. In S. Jialin Pan & M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
(Vol. 129, pp. 49–64). Bangkok, Thailand: PMLR.'
bibtex: '@inproceedings{Damke_Melnikov_Hüllermeier_2020, place={Bangkok, Thailand},
series={Proceedings of Machine Learning Research}, title={A Novel Higher-order
Weisfeiler-Lehman Graph Convolution}, volume={129}, booktitle={Proceedings of
the 12th Asian Conference on Machine Learning (ACML 2020)}, publisher={PMLR},
author={Damke, Clemens and Melnikov, Vitaly and Hüllermeier, Eyke}, editor={Jialin
Pan, Sinno and Sugiyama, MasashiEditors}, year={2020}, pages={49–64}, collection={Proceedings
of Machine Learning Research} }'
chicago: 'Damke, Clemens, Vitaly Melnikov, and Eyke Hüllermeier. “A Novel Higher-Order
Weisfeiler-Lehman Graph Convolution.” In Proceedings of the 12th Asian Conference
on Machine Learning (ACML 2020), edited by Sinno Jialin Pan and Masashi Sugiyama,
129:49–64. Proceedings of Machine Learning Research. Bangkok, Thailand: PMLR,
2020.'
ieee: C. Damke, V. Melnikov, and E. Hüllermeier, “A Novel Higher-order Weisfeiler-Lehman
Graph Convolution,” in Proceedings of the 12th Asian Conference on Machine
Learning (ACML 2020), Bangkok, Thailand, 2020, vol. 129, pp. 49–64.
mla: Damke, Clemens, et al. “A Novel Higher-Order Weisfeiler-Lehman Graph Convolution.”
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020),
edited by Sinno Jialin Pan and Masashi Sugiyama, vol. 129, PMLR, 2020, pp. 49–64.
short: 'C. Damke, V. Melnikov, E. Hüllermeier, in: S. Jialin Pan, M. Sugiyama (Eds.),
Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020), PMLR,
Bangkok, Thailand, 2020, pp. 49–64.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-10-08T10:48:38Z
date_updated: 2022-01-06T06:54:17Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Sinno
full_name: Jialin Pan, Sinno
last_name: Jialin Pan
- first_name: Masashi
full_name: Sugiyama, Masashi
last_name: Sugiyama
external_id:
arxiv:
- '2007.00346'
file:
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:48Z
date_updated: 2020-10-08T11:21:00Z
file_id: '19954'
file_name: damke20.pdf
file_size: 771137
relation: main_file
- access_level: open_access
content_type: application/pdf
creator: cdamke
date_created: 2020-10-08T10:54:59Z
date_updated: 2020-10-08T11:24:29Z
file_id: '19955'
file_name: damke20-supp.pdf
file_size: 613163
relation: supplementary_material
file_date_updated: 2020-10-08T11:24:29Z
has_accepted_license: '1'
intvolume: ' 129'
keyword:
- graph neural networks
- Weisfeiler-Lehman test
- cycle detection
language:
- iso: eng
oa: '1'
page: 49-64
place: Bangkok, Thailand
publication: Proceedings of the 12th Asian Conference on Machine Learning (ACML 2020)
publication_status: published
publisher: PMLR
quality_controlled: '1'
series_title: Proceedings of Machine Learning Research
status: public
title: A Novel Higher-order Weisfeiler-Lehman Graph Convolution
type: conference
user_id: '48192'
volume: 129
year: '2020'
...
---
_id: '21534'
author:
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Bengs V, Hüllermeier E. Preselection Bandits. In: International Conference
on Machine Learning. ; 2020:778-787.'
apa: Bengs, V., & Hüllermeier, E. (2020). Preselection Bandits. In International
Conference on Machine Learning (pp. 778–787).
bibtex: '@inproceedings{Bengs_Hüllermeier_2020, title={Preselection Bandits}, booktitle={International
Conference on Machine Learning}, author={Bengs, Viktor and Hüllermeier, Eyke},
year={2020}, pages={778–787} }'
chicago: Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” In International
Conference on Machine Learning, 778–87, 2020.
ieee: V. Bengs and E. Hüllermeier, “Preselection Bandits,” in International Conference
on Machine Learning, 2020, pp. 778–787.
mla: Bengs, Viktor, and Eyke Hüllermeier. “Preselection Bandits.” International
Conference on Machine Learning, 2020, pp. 778–87.
short: 'V. Bengs, E. Hüllermeier, in: International Conference on Machine Learning,
2020, pp. 778–787.'
date_created: 2021-03-18T11:13:12Z
date_updated: 2022-01-06T06:55:03Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
page: 778-787
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: International Conference on Machine Learning
status: public
title: Preselection Bandits
type: conference
user_id: '76599'
year: '2020'
...
---
_id: '21536'
abstract:
- lang: eng
text: "We consider a resource-aware variant of the classical multi-armed bandit\r\nproblem:
In each round, the learner selects an arm and determines a resource\r\nlimit.
It then observes a corresponding (random) reward, provided the (random)\r\namount
of consumed resources remains below the limit. Otherwise, the\r\nobservation is
censored, i.e., no reward is obtained. For this problem setting,\r\nwe introduce
a measure of regret, which incorporates the actual amount of\r\nallocated resources
of each learning round as well as the optimality of\r\nrealizable rewards. Thus,
to minimize regret, the learner needs to set a\r\nresource limit and choose an
arm in such a way that the chance to realize a\r\nhigh reward within the predefined
resource limit is high, while the resource\r\nlimit itself should be kept as low
as possible. We derive the theoretical lower\r\nbound on the cumulative regret
and propose a learning algorithm having a regret\r\nupper bound that matches the
lower bound. In a simulation study, we show that\r\nour learning algorithm outperforms
straightforward extensions of standard\r\nmulti-armed bandit algorithms."
author:
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: Bengs V, Hüllermeier E. Multi-Armed Bandits with Censored Consumption of Resources.
arXiv:201100813. 2020.
apa: Bengs, V., & Hüllermeier, E. (2020). Multi-Armed Bandits with Censored
Consumption of Resources. ArXiv:2011.00813.
bibtex: '@article{Bengs_Hüllermeier_2020, title={Multi-Armed Bandits with Censored
Consumption of Resources}, journal={arXiv:2011.00813}, author={Bengs, Viktor and
Hüllermeier, Eyke}, year={2020} }'
chicago: Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored
Consumption of Resources.” ArXiv:2011.00813, 2020.
ieee: V. Bengs and E. Hüllermeier, “Multi-Armed Bandits with Censored Consumption
of Resources,” arXiv:2011.00813. 2020.
mla: Bengs, Viktor, and Eyke Hüllermeier. “Multi-Armed Bandits with Censored Consumption
of Resources.” ArXiv:2011.00813, 2020.
short: V. Bengs, E. Hüllermeier, ArXiv:2011.00813 (2020).
date_created: 2021-03-18T11:27:37Z
date_updated: 2022-01-06T06:55:03Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: arXiv:2011.00813
status: public
title: Multi-Armed Bandits with Censored Consumption of Resources
type: preprint
user_id: '76599'
year: '2020'
...
---
_id: '17407'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Extreme Algorithm Selection with Dyadic
Feature Representation. In: Discovery Science. ; 2020.'
apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Extreme Algorithm
Selection with Dyadic Feature Representation. Discovery Science. Discovery
Science 2020.
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Extreme Algorithm
Selection with Dyadic Feature Representation}, booktitle={Discovery Science},
author={Tornede, Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020}
}'
chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Extreme
Algorithm Selection with Dyadic Feature Representation.” In Discovery Science,
2020.
ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Extreme Algorithm Selection
with Dyadic Feature Representation,” presented at the Discovery Science 2020,
2020.
mla: Tornede, Alexander, et al. “Extreme Algorithm Selection with Dyadic Feature
Representation.” Discovery Science, 2020.
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Discovery Science, 2020.'
conference:
name: Discovery Science 2020
date_created: 2020-07-21T10:06:51Z
date_updated: 2022-01-06T06:53:10Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Discovery Science
status: public
title: Extreme Algorithm Selection with Dyadic Feature Representation
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17408'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Tornede A, Wever MD, Hüllermeier E. Hybrid Ranking and Regression
for Algorithm Selection. In: KI 2020: Advances in Artificial Intelligence.
; 2020.'
apa: 'Hanselle, J. M., Tornede, A., Wever, M. D., & Hüllermeier, E. (2020).
Hybrid Ranking and Regression for Algorithm Selection. KI 2020: Advances in
Artificial Intelligence. 43rd German Conference on Artificial Intelligence.'
bibtex: '@inproceedings{Hanselle_Tornede_Wever_Hüllermeier_2020, title={Hybrid Ranking
and Regression for Algorithm Selection}, booktitle={KI 2020: Advances in Artificial
Intelligence}, author={Hanselle, Jonas Manuel and Tornede, Alexander and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2020} }'
chicago: 'Hanselle, Jonas Manuel, Alexander Tornede, Marcel Dominik Wever, and Eyke
Hüllermeier. “Hybrid Ranking and Regression for Algorithm Selection.” In KI
2020: Advances in Artificial Intelligence, 2020.'
ieee: J. M. Hanselle, A. Tornede, M. D. Wever, and E. Hüllermeier, “Hybrid Ranking
and Regression for Algorithm Selection,” presented at the 43rd German Conference
on Artificial Intelligence, 2020.
mla: 'Hanselle, Jonas Manuel, et al. “Hybrid Ranking and Regression for Algorithm
Selection.” KI 2020: Advances in Artificial Intelligence, 2020.'
short: 'J.M. Hanselle, A. Tornede, M.D. Wever, E. Hüllermeier, in: KI 2020: Advances
in Artificial Intelligence, 2020.'
conference:
name: 43rd German Conference on Artificial Intelligence
date_created: 2020-07-21T10:21:09Z
date_updated: 2022-01-06T06:53:10Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: 'KI 2020: Advances in Artificial Intelligence'
status: public
title: Hybrid Ranking and Regression for Algorithm Selection
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17424'
author:
- first_name: Tanja
full_name: Tornede, Tanja
id: '40795'
last_name: Tornede
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede T, Tornede A, Wever MD, Mohr F, Hüllermeier E. AutoML for Predictive
Maintenance: One Tool to RUL Them All. In: Proceedings of the ECMLPKDD 2020.
; 2020. doi:10.1007/978-3-030-66770-2_8'
apa: 'Tornede, T., Tornede, A., Wever, M. D., Mohr, F., & Hüllermeier, E. (2020).
AutoML for Predictive Maintenance: One Tool to RUL Them All. Proceedings of
the ECMLPKDD 2020. IOTStream Workshop @ ECMLPKDD 2020. https://doi.org/10.1007/978-3-030-66770-2_8'
bibtex: '@inproceedings{Tornede_Tornede_Wever_Mohr_Hüllermeier_2020, title={AutoML
for Predictive Maintenance: One Tool to RUL Them All}, DOI={10.1007/978-3-030-66770-2_8},
booktitle={Proceedings of the ECMLPKDD 2020}, author={Tornede, Tanja and Tornede,
Alexander and Wever, Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2020}
}'
chicago: 'Tornede, Tanja, Alexander Tornede, Marcel Dominik Wever, Felix Mohr, and
Eyke Hüllermeier. “AutoML for Predictive Maintenance: One Tool to RUL Them All.”
In Proceedings of the ECMLPKDD 2020, 2020. https://doi.org/10.1007/978-3-030-66770-2_8.'
ieee: 'T. Tornede, A. Tornede, M. D. Wever, F. Mohr, and E. Hüllermeier, “AutoML
for Predictive Maintenance: One Tool to RUL Them All,” presented at the IOTStream
Workshop @ ECMLPKDD 2020, 2020, doi: 10.1007/978-3-030-66770-2_8.'
mla: 'Tornede, Tanja, et al. “AutoML for Predictive Maintenance: One Tool to RUL
Them All.” Proceedings of the ECMLPKDD 2020, 2020, doi:10.1007/978-3-030-66770-2_8.'
short: 'T. Tornede, A. Tornede, M.D. Wever, F. Mohr, E. Hüllermeier, in: Proceedings
of the ECMLPKDD 2020, 2020.'
conference:
name: IOTStream Workshop @ ECMLPKDD 2020
date_created: 2020-07-28T09:17:41Z
date_updated: 2022-01-06T06:53:11Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
doi: 10.1007/978-3-030-66770-2_8
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '1'
name: SFB 901
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings of the ECMLPKDD 2020
status: public
title: 'AutoML for Predictive Maintenance: One Tool to RUL Them All'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '17605'
abstract:
- lang: eng
text: "Syntactic annotation of corpora in the form of part-of-speech (POS) tags
is a key requirement for both linguistic research and subsequent automated natural
language processing (NLP) tasks. This problem is commonly tackled using machine
learning methods, i.e., by training a POS tagger on a sufficiently large corpus
of labeled data. \r\nWhile the problem of POS tagging can essentially be considered
as solved for modern languages, historical corpora turn out to be much more difficult,
especially due to the lack of native speakers and sparsity of training data. Moreover,
most texts have no sentences as we know them today, nor a common orthography.\r\nThese
irregularities render the task of automated POS tagging more difficult and error-prone.
Under these circumstances, instead of forcing the POS tagger to predict and commit
to a single tag, it should be enabled to express its uncertainty. In this paper,
we consider POS tagging within the framework of set-valued prediction, which allows
the POS tagger to express its uncertainty via predicting a set of candidate POS
tags instead of guessing a single one. The goal is to guarantee a high confidence
that the correct POS tag is included while keeping the number of candidates small.\r\nIn
our experimental study, we find that extending state-of-the-art POS taggers to
set-valued prediction yields more precise and robust taggings, especially for
unknown words, i.e., words not occurring in the training data."
author:
- first_name: Stefan Helmut
full_name: Heid, Stefan Helmut
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Heid SH, Wever MD, Hüllermeier E. Reliable Part-of-Speech Tagging of Historical
Corpora through Set-Valued Prediction. Journal of Data Mining and Digital Humanities.
apa: Heid, S. H., Wever, M. D., & Hüllermeier, E. (n.d.). Reliable Part-of-Speech
Tagging of Historical Corpora through Set-Valued Prediction. In Journal of
Data Mining and Digital Humanities. episciences.
bibtex: '@article{Heid_Wever_Hüllermeier, title={Reliable Part-of-Speech Tagging
of Historical Corpora through Set-Valued Prediction}, journal={Journal of Data
Mining and Digital Humanities}, publisher={episciences}, author={Heid, Stefan
Helmut and Wever, Marcel Dominik and Hüllermeier, Eyke} }'
chicago: Heid, Stefan Helmut, Marcel Dominik Wever, and Eyke Hüllermeier. “Reliable
Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction.” Journal
of Data Mining and Digital Humanities. episciences, n.d.
ieee: S. H. Heid, M. D. Wever, and E. Hüllermeier, “Reliable Part-of-Speech Tagging
of Historical Corpora through Set-Valued Prediction,” Journal of Data Mining
and Digital Humanities. episciences.
mla: Heid, Stefan Helmut, et al. “Reliable Part-of-Speech Tagging of Historical
Corpora through Set-Valued Prediction.” Journal of Data Mining and Digital
Humanities, episciences.
short: S.H. Heid, M.D. Wever, E. Hüllermeier, Journal of Data Mining and Digital
Humanities (n.d.).
date_created: 2020-08-05T06:52:53Z
date_updated: 2022-01-06T06:53:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2008.01377
oa: '1'
project:
- _id: '39'
name: InterGramm
publication: Journal of Data Mining and Digital Humanities
publication_status: submitted
publisher: episciences
status: public
title: Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction
type: preprint
user_id: '5786'
year: '2020'
...
---
_id: '20306'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Towards Meta-Algorithm Selection. In:
Workshop MetaLearn 2020 @ NeurIPS 2020. ; 2020.'
apa: Tornede, A., Wever, M. D., & Hüllermeier, E. (2020). Towards Meta-Algorithm
Selection. Workshop MetaLearn 2020 @ NeurIPS 2020. Workshop MetaLearn 2020
@ NeurIPS 2020, Online.
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2020, title={Towards Meta-Algorithm
Selection}, booktitle={Workshop MetaLearn 2020 @ NeurIPS 2020}, author={Tornede,
Alexander and Wever, Marcel Dominik and Hüllermeier, Eyke}, year={2020} }'
chicago: Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Towards
Meta-Algorithm Selection.” In Workshop MetaLearn 2020 @ NeurIPS 2020, 2020.
ieee: A. Tornede, M. D. Wever, and E. Hüllermeier, “Towards Meta-Algorithm Selection,”
presented at the Workshop MetaLearn 2020 @ NeurIPS 2020, Online, 2020.
mla: Tornede, Alexander, et al. “Towards Meta-Algorithm Selection.” Workshop
MetaLearn 2020 @ NeurIPS 2020, 2020.
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: Workshop MetaLearn 2020 @ NeurIPS
2020, 2020.'
conference:
location: Online
name: Workshop MetaLearn 2020 @ NeurIPS 2020
date_created: 2020-11-06T09:42:27Z
date_updated: 2022-01-06T06:54:26Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Workshop MetaLearn 2020 @ NeurIPS 2020
status: public
title: Towards Meta-Algorithm Selection
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '18014'
author:
- first_name: Adil
full_name: El Mesaoudi-Paul, Adil
last_name: El Mesaoudi-Paul
- first_name: Dimitri
full_name: Weiß, Dimitri
last_name: Weiß
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Kevin
full_name: Tierney, Kevin
last_name: Tierney
citation:
ama: 'El Mesaoudi-Paul A, Weiß D, Bengs V, Hüllermeier E, Tierney K. Pool-Based
Realtime Algorithm Configuration: A Preselection Bandit Approach. In: Learning
and Intelligent Optimization. LION 2020. Vol 12096. Lecture Notes in Computer
Science. Cham: Springer; 2020:216-232. doi:10.1007/978-3-030-53552-0_22'
apa: 'El Mesaoudi-Paul, A., Weiß, D., Bengs, V., Hüllermeier, E., & Tierney,
K. (2020). Pool-Based Realtime Algorithm Configuration: A Preselection Bandit
Approach. In Learning and Intelligent Optimization. LION 2020. (Vol. 12096,
pp. 216–232). Cham: Springer. https://doi.org/10.1007/978-3-030-53552-0_22'
bibtex: '@inbook{El Mesaoudi-Paul_Weiß_Bengs_Hüllermeier_Tierney_2020, place={Cham},
series={Lecture Notes in Computer Science}, title={Pool-Based Realtime Algorithm
Configuration: A Preselection Bandit Approach}, volume={12096}, DOI={10.1007/978-3-030-53552-0_22},
booktitle={Learning and Intelligent Optimization. LION 2020.}, publisher={Springer},
author={El Mesaoudi-Paul, Adil and Weiß, Dimitri and Bengs, Viktor and Hüllermeier,
Eyke and Tierney, Kevin}, year={2020}, pages={216–232}, collection={Lecture Notes
in Computer Science} }'
chicago: 'El Mesaoudi-Paul, Adil, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier,
and Kevin Tierney. “Pool-Based Realtime Algorithm Configuration: A Preselection
Bandit Approach.” In Learning and Intelligent Optimization. LION 2020.,
12096:216–32. Lecture Notes in Computer Science. Cham: Springer, 2020. https://doi.org/10.1007/978-3-030-53552-0_22.'
ieee: 'A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, and K. Tierney, “Pool-Based
Realtime Algorithm Configuration: A Preselection Bandit Approach,” in Learning
and Intelligent Optimization. LION 2020., vol. 12096, Cham: Springer, 2020,
pp. 216–232.'
mla: 'El Mesaoudi-Paul, Adil, et al. “Pool-Based Realtime Algorithm Configuration:
A Preselection Bandit Approach.” Learning and Intelligent Optimization. LION
2020., vol. 12096, Springer, 2020, pp. 216–32, doi:10.1007/978-3-030-53552-0_22.'
short: 'A. El Mesaoudi-Paul, D. Weiß, V. Bengs, E. Hüllermeier, K. Tierney, in:
Learning and Intelligent Optimization. LION 2020., Springer, Cham, 2020, pp. 216–232.'
date_created: 2020-08-17T11:44:37Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
doi: 10.1007/978-3-030-53552-0_22
intvolume: ' 12096'
language:
- iso: eng
page: 216 - 232
place: Cham
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Learning and Intelligent Optimization. LION 2020.
publication_identifier:
isbn:
- '9783030535513'
- '9783030535520'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer
series_title: Lecture Notes in Computer Science
status: public
title: 'Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach'
type: book_chapter
user_id: '76599'
volume: 12096
year: '2020'
...
---
_id: '18017'
abstract:
- lang: eng
text: "We consider an extension of the contextual multi-armed bandit problem, in\r\nwhich,
instead of selecting a single alternative (arm), a learner is supposed\r\nto make
a preselection in the form of a subset of alternatives. More\r\nspecifically,
in each iteration, the learner is presented a set of arms and a\r\ncontext, both
described in terms of feature vectors. The task of the learner is\r\nto preselect
$k$ of these arms, among which a final choice is made in a second\r\nstep. In
our setup, we assume that each arm has a latent (context-dependent)\r\nutility,
and that feedback on a preselection is produced according to a\r\nPlackett-Luce
model. We propose the CPPL algorithm, which is inspired by the\r\nwell-known UCB
algorithm, and evaluate this algorithm on synthetic and real\r\ndata. In particular,
we consider an online algorithm selection scenario, which\r\nserved as a main
motivation of our problem setting. Here, an instance (which\r\ndefines the context)
from a certain problem class (such as SAT) can be solved\r\nby different algorithms
(the arms), but only $k$ of these algorithms can\r\nactually be run."
author:
- first_name: Adil
full_name: El Mesaoudi-Paul, Adil
last_name: El Mesaoudi-Paul
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: El Mesaoudi-Paul A, Bengs V, Hüllermeier E. Online Preselection with Context
Information under the Plackett-Luce Model. arXiv:200204275.
apa: El Mesaoudi-Paul, A., Bengs, V., & Hüllermeier, E. (n.d.). Online Preselection
with Context Information under the Plackett-Luce Model. ArXiv:2002.04275.
bibtex: '@article{El Mesaoudi-Paul_Bengs_Hüllermeier, title={Online Preselection
with Context Information under the Plackett-Luce Model}, journal={arXiv:2002.04275},
author={El Mesaoudi-Paul, Adil and Bengs, Viktor and Hüllermeier, Eyke} }'
chicago: El Mesaoudi-Paul, Adil, Viktor Bengs, and Eyke Hüllermeier. “Online Preselection
with Context Information under the Plackett-Luce Model.” ArXiv:2002.04275,
n.d.
ieee: A. El Mesaoudi-Paul, V. Bengs, and E. Hüllermeier, “Online Preselection with
Context Information under the Plackett-Luce Model,” arXiv:2002.04275.
.
mla: El Mesaoudi-Paul, Adil, et al. “Online Preselection with Context Information
under the Plackett-Luce Model.” ArXiv:2002.04275.
short: A. El Mesaoudi-Paul, V. Bengs, E. Hüllermeier, ArXiv:2002.04275 (n.d.).
date_created: 2020-08-17T11:49:40Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: arXiv:2002.04275
publication_status: draft
status: public
title: Online Preselection with Context Information under the Plackett-Luce Model
type: preprint
user_id: '76599'
year: '2020'
...
---
_id: '18276'
abstract:
- lang: eng
text: "Algorithm selection (AS) deals with the automatic selection of an algorithm\r\nfrom
a fixed set of candidate algorithms most suitable for a specific instance\r\nof
an algorithmic problem class, where \"suitability\" often refers to an\r\nalgorithm's
runtime. Due to possibly extremely long runtimes of candidate\r\nalgorithms, training
data for algorithm selection models is usually generated\r\nunder time constraints
in the sense that not all algorithms are run to\r\ncompletion on all instances.
Thus, training data usually comprises censored\r\ninformation, as the true runtime
of algorithms timed out remains unknown.\r\nHowever, many standard AS approaches
are not able to handle such information in\r\na proper way. On the other side,
survival analysis (SA) naturally supports\r\ncensored data and offers appropriate
ways to use such data for learning\r\ndistributional models of algorithm runtime,
as we demonstrate in this work. We\r\nleverage such models as a basis of a sophisticated
decision-theoretic approach\r\nto algorithm selection, which we dub Run2Survive.
Moreover, taking advantage of\r\na framework of this kind, we advocate a risk-averse
approach to algorithm\r\nselection, in which the avoidance of a timeout is given
high priority. In an\r\nextensive experimental study with the standard benchmark
ASlib, our approach is\r\nshown to be highly competitive and in many cases even
superior to\r\nstate-of-the-art AS approaches."
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Stefan
full_name: Werner, Stefan
last_name: Werner
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Werner S, Mohr F, Hüllermeier E. Run2Survive: A Decision-theoretic
Approach to Algorithm Selection based on Survival Analysis. In: ACML 2020.
; 2020.'
apa: 'Tornede, A., Wever, M. D., Werner, S., Mohr, F., & Hüllermeier, E. (2020).
Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival
Analysis. ACML 2020. 12th Asian Conference on Machine Learning, Bangkok,
Thailand.'
bibtex: '@inproceedings{Tornede_Wever_Werner_Mohr_Hüllermeier_2020, title={Run2Survive:
A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis},
booktitle={ACML 2020}, author={Tornede, Alexander and Wever, Marcel Dominik and
Werner, Stefan and Mohr, Felix and Hüllermeier, Eyke}, year={2020} }'
chicago: 'Tornede, Alexander, Marcel Dominik Wever, Stefan Werner, Felix Mohr, and
Eyke Hüllermeier. “Run2Survive: A Decision-Theoretic Approach to Algorithm Selection
Based on Survival Analysis.” In ACML 2020, 2020.'
ieee: 'A. Tornede, M. D. Wever, S. Werner, F. Mohr, and E. Hüllermeier, “Run2Survive:
A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis,”
presented at the 12th Asian Conference on Machine Learning, Bangkok, Thailand,
2020.'
mla: 'Tornede, Alexander, et al. “Run2Survive: A Decision-Theoretic Approach to
Algorithm Selection Based on Survival Analysis.” ACML 2020, 2020.'
short: 'A. Tornede, M.D. Wever, S. Werner, F. Mohr, E. Hüllermeier, in: ACML 2020,
2020.'
conference:
end_date: 2020-11-20
location: Bangkok, Thailand
name: 12th Asian Conference on Machine Learning
start_date: 2020-11-18
date_created: 2020-08-25T12:09:28Z
date_updated: 2022-01-06T06:53:28Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
main_file_link:
- url: https://arxiv.org/pdf/2007.02816.pdf
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: ACML 2020
status: public
title: 'Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on
Survival Analysis'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '16725'
author:
- first_name: Cedric
full_name: Richter, Cedric
id: '50003'
last_name: Richter
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marie-Christine
full_name: Jakobs, Marie-Christine
last_name: Jakobs
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
citation:
ama: Richter C, Hüllermeier E, Jakobs M-C, Wehrheim H. Algorithm Selection for Software
Validation Based on Graph Kernels. Journal of Automated Software Engineering.
apa: Richter, C., Hüllermeier, E., Jakobs, M.-C., & Wehrheim, H. (n.d.). Algorithm
Selection for Software Validation Based on Graph Kernels. Journal of Automated
Software Engineering.
bibtex: '@article{Richter_Hüllermeier_Jakobs_Wehrheim, title={Algorithm Selection
for Software Validation Based on Graph Kernels}, journal={Journal of Automated
Software Engineering}, publisher={Springer}, author={Richter, Cedric and Hüllermeier,
Eyke and Jakobs, Marie-Christine and Wehrheim, Heike} }'
chicago: Richter, Cedric, Eyke Hüllermeier, Marie-Christine Jakobs, and Heike Wehrheim.
“Algorithm Selection for Software Validation Based on Graph Kernels.” Journal
of Automated Software Engineering, n.d.
ieee: C. Richter, E. Hüllermeier, M.-C. Jakobs, and H. Wehrheim, “Algorithm Selection
for Software Validation Based on Graph Kernels,” Journal of Automated Software
Engineering.
mla: Richter, Cedric, et al. “Algorithm Selection for Software Validation Based
on Graph Kernels.” Journal of Automated Software Engineering, Springer.
short: C. Richter, E. Hüllermeier, M.-C. Jakobs, H. Wehrheim, Journal of Automated
Software Engineering (n.d.).
date_created: 2020-04-19T14:08:06Z
date_updated: 2022-01-06T06:52:55Z
department:
- _id: '7'
- _id: '77'
- _id: '355'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '12'
name: SFB 901 - Subproject B4
publication: Journal of Automated Software Engineering
publication_status: accepted
publisher: Springer
status: public
title: Algorithm Selection for Software Validation Based on Graph Kernels
type: journal_article
user_id: '477'
year: '2020'
...
---
_id: '15629'
abstract:
- lang: eng
text: In multi-label classification (MLC), each instance is associated with a set
of class labels, in contrast to standard classification where an instance is assigned
a single label. Binary relevance (BR) learning, which reduces a multi-label to
a set of binary classification problems, one per label, is arguably the most straight-forward
approach to MLC. In spite of its simplicity, BR proved to be competitive to more
sophisticated MLC methods, and still achieves state-of-the-art performance for
many loss functions. Somewhat surprisingly, the optimal choice of the base learner
for tackling the binary classification problems has received very little attention
so far. Taking advantage of the label independence assumption inherent to BR,
we propose a label-wise base learner selection method optimizing label-wise macro
averaged performance measures. In an extensive experimental evaluation, we find
that or approach, called LiBRe, can significantly improve generalization performance.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Tornede A, Mohr F, Hüllermeier E. LiBRe: Label-Wise Selection of
Base Learners in Binary Relevance for Multi-Label Classification. In: Springer.'
apa: 'Wever, M. D., Tornede, A., Mohr, F., & Hüllermeier, E. (n.d.). LiBRe:
Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label Classification.
Symposium on Intelligent Data Analysis, Konstanz, Germany.'
bibtex: '@inproceedings{Wever_Tornede_Mohr_Hüllermeier, title={LiBRe: Label-Wise
Selection of Base Learners in Binary Relevance for Multi-Label Classification},
publisher={Springer}, author={Wever, Marcel Dominik and Tornede, Alexander and
Mohr, Felix and Hüllermeier, Eyke} }'
chicago: 'Wever, Marcel Dominik, Alexander Tornede, Felix Mohr, and Eyke Hüllermeier.
“LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label
Classification.” Springer, n.d.'
ieee: 'M. D. Wever, A. Tornede, F. Mohr, and E. Hüllermeier, “LiBRe: Label-Wise
Selection of Base Learners in Binary Relevance for Multi-Label Classification,”
presented at the Symposium on Intelligent Data Analysis, Konstanz, Germany.'
mla: 'Wever, Marcel Dominik, et al. LiBRe: Label-Wise Selection of Base Learners
in Binary Relevance for Multi-Label Classification. Springer.'
short: 'M.D. Wever, A. Tornede, F. Mohr, E. Hüllermeier, in: Springer, n.d.'
conference:
end_date: 2020-04-27
location: Konstanz, Germany
name: Symposium on Intelligent Data Analysis
start_date: 2020-04-24
date_created: 2020-01-23T08:44:08Z
date_updated: 2022-01-06T06:52:30Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication_status: accepted
publisher: Springer
status: public
title: 'LiBRe: Label-Wise Selection of Base Learners in Binary Relevance for Multi-Label
Classification'
type: conference
user_id: '5786'
year: '2020'
...
---
_id: '15025'
abstract:
- lang: eng
text: In software engineering, the imprecise requirements of a user are transformed
to a formal requirements specification during the requirements elicitation process.
This process is usually guided by requirements engineers interviewing the user.
We want to partially automate this first step of the software engineering process
in order to enable users to specify a desired software system on their own. With
our approach, users are only asked to provide exemplary behavioral descriptions.
The problem of synthesizing a requirements specification from examples can partially
be reduced to the problem of grammatical inference, to which we apply an active
coevolutionary learning approach. However, this approach would usually require
many feedback queries to be sent to the user. In this work, we extend and generalize
our active learning approach to receive knowledge from multiple oracles, also
known as proactive learning. The ‘user oracle’ represents input received from
the user and the ‘knowledge oracle’ represents available, formalized domain knowledge.
We call our two-oracle approach the ‘first apply knowledge then query’ (FAKT/Q)
algorithm. We compare FAKT/Q to the active learning approach and provide an extensive
benchmark evaluation. As result we find that the number of required user queries
is reduced and the inference process is sped up significantly. Finally, with so-called
On-The-Fly Markets, we present a motivation and an application of our approach
where such knowledge is available.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Lorijn
full_name: van Rooijen, Lorijn
id: '58843'
last_name: van Rooijen
- first_name: Heiko
full_name: Hamann, Heiko
last_name: Hamann
citation:
ama: Wever MD, van Rooijen L, Hamann H. Multi-Oracle Coevolutionary Learning of
Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary
Computation. 2020;28(2):165–193. doi:10.1162/evco_a_00266
apa: Wever, M. D., van Rooijen, L., & Hamann, H. (2020). Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets. Evolutionary
Computation, 28(2), 165–193. https://doi.org/10.1162/evco_a_00266
bibtex: '@article{Wever_van Rooijen_Hamann_2020, title={Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets},
volume={28}, DOI={10.1162/evco_a_00266},
number={2}, journal={Evolutionary Computation}, publisher={MIT Press Journals},
author={Wever, Marcel Dominik and van Rooijen, Lorijn and Hamann, Heiko}, year={2020},
pages={165–193} }'
chicago: 'Wever, Marcel Dominik, Lorijn van Rooijen, and Heiko Hamann. “Multi-Oracle
Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly
Markets.” Evolutionary Computation 28, no. 2 (2020): 165–193. https://doi.org/10.1162/evco_a_00266.'
ieee: 'M. D. Wever, L. van Rooijen, and H. Hamann, “Multi-Oracle Coevolutionary
Learning of Requirements Specifications from Examples in On-The-Fly Markets,”
Evolutionary Computation, vol. 28, no. 2, pp. 165–193, 2020, doi: 10.1162/evco_a_00266.'
mla: Wever, Marcel Dominik, et al. “Multi-Oracle Coevolutionary Learning of Requirements
Specifications from Examples in On-The-Fly Markets.” Evolutionary Computation,
vol. 28, no. 2, MIT Press Journals, 2020, pp. 165–193, doi:10.1162/evco_a_00266.
short: M.D. Wever, L. van Rooijen, H. Hamann, Evolutionary Computation 28 (2020)
165–193.
date_created: 2019-11-18T14:19:19Z
date_updated: 2022-01-06T06:52:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
- _id: '63'
- _id: '238'
doi: 10.1162/evco_a_00266
intvolume: ' 28'
issue: '2'
language:
- iso: eng
page: 165–193
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '9'
name: SFB 901 - Subproject B1
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Evolutionary Computation
publication_status: published
publisher: MIT Press Journals
related_material:
link:
- relation: confirmation
url: https://www.mitpressjournals.org/doi/pdf/10.1162/evco_a_00266
status: public
title: Multi-Oracle Coevolutionary Learning of Requirements Specifications from Examples
in On-The-Fly Markets
type: journal_article
user_id: '15415'
volume: 28
year: '2020'
...
---
_id: '19523'
abstract:
- lang: eng
text: "We study the problem of learning choice functions, which play an important\r\nrole
in various domains of application, most notably in the field of economics.\r\nFormally,
a choice function is a mapping from sets to sets: Given a set of\r\nchoice alternatives
as input, a choice function identifies a subset of most\r\npreferred elements.
Learning choice functions from suitable training data comes\r\nwith a number of
challenges. For example, the sets provided as input and the\r\nsubsets produced
as output can be of any size. Moreover, since the order in\r\nwhich alternatives
are presented is irrelevant, a choice function should be\r\nsymmetric. Perhaps
most importantly, choice functions are naturally\r\ncontext-dependent, in the
sense that the preference in favor of an alternative\r\nmay depend on what other
options are available. We formalize the problem of\r\nlearning choice functions
and present two general approaches based on two\r\nrepresentations of context-dependent
utility functions. Both approaches are\r\ninstantiated by means of appropriate
neural network architectures, and their\r\nperformance is demonstrated on suitable
benchmark tasks."
author:
- first_name: Karlson
full_name: Pfannschmidt, Karlson
last_name: Pfannschmidt
- first_name: Pritha
full_name: Gupta, Pritha
last_name: Gupta
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: 'Pfannschmidt K, Gupta P, Hüllermeier E. Learning Choice Functions: Concepts
and Architectures. arXiv:190110860. 2019.'
apa: 'Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2019). Learning Choice
Functions: Concepts and Architectures. ArXiv:1901.10860.'
bibtex: '@article{Pfannschmidt_Gupta_Hüllermeier_2019, title={Learning Choice Functions:
Concepts and Architectures}, journal={arXiv:1901.10860}, author={Pfannschmidt,
Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Learning Choice
Functions: Concepts and Architectures.” ArXiv:1901.10860, 2019.'
ieee: 'K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Learning Choice Functions:
Concepts and Architectures,” arXiv:1901.10860. 2019.'
mla: 'Pfannschmidt, Karlson, et al. “Learning Choice Functions: Concepts and Architectures.”
ArXiv:1901.10860, 2019.'
short: K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1901.10860 (2019).
date_created: 2020-09-17T10:53:38Z
date_updated: 2022-01-06T06:54:06Z
department:
- _id: '7'
- _id: '355'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: arXiv:1901.10860
status: public
title: 'Learning Choice Functions: Concepts and Architectures'
type: preprint
user_id: '13472'
year: '2019'
...
---
_id: '17565'
author:
- first_name: Marie-Luis
full_name: Merten, Marie-Luis
last_name: Merten
- first_name: Nina
full_name: Seemann, Nina
last_name: Seemann
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: Merten M-L, Seemann N, Wever MD. Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches
Jahrbuch. 2019;(142):124-146.
apa: Merten, M.-L., Seemann, N., & Wever, M. D. (2019). Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff. Niederdeutsches
Jahrbuch, 142, 124–146.
bibtex: '@article{Merten_Seemann_Wever_2019, title={Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff},
number={142}, journal={Niederdeutsches Jahrbuch}, author={Merten, Marie-Luis and
Seemann, Nina and Wever, Marcel Dominik}, year={2019}, pages={124–146} }'
chicago: 'Merten, Marie-Luis, Nina Seemann, and Marcel Dominik Wever. “Grammatikwandel
digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher Sprachausbau im
interdisziplinären Zugriff.” Niederdeutsches Jahrbuch, no. 142 (2019):
124–46.'
ieee: M.-L. Merten, N. Seemann, and M. D. Wever, “Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff,”
Niederdeutsches Jahrbuch, no. 142, pp. 124–146, 2019.
mla: Merten, Marie-Luis, et al. “Grammatikwandel digital-kulturwissenschaftlich
erforscht. Mittelniederdeutscher Sprachausbau im interdisziplinären Zugriff.”
Niederdeutsches Jahrbuch, no. 142, 2019, pp. 124–46.
short: M.-L. Merten, N. Seemann, M.D. Wever, Niederdeutsches Jahrbuch (2019) 124–146.
date_created: 2020-08-03T13:55:04Z
date_updated: 2022-01-06T06:53:15Z
department:
- _id: '34'
- _id: '355'
- _id: '26'
issue: '142'
language:
- iso: ger
page: 124-146
project:
- _id: '39'
name: InterGramm
publication: Niederdeutsches Jahrbuch
publication_status: published
status: public
title: Grammatikwandel digital-kulturwissenschaftlich erforscht. Mittelniederdeutscher
Sprachausbau im interdisziplinären Zugriff
type: journal_article
user_id: '5786'
year: '2019'
...
---
_id: '18018'
abstract:
- lang: eng
text: |-
A common statistical task lies in showing asymptotic normality of certain
statistics. In many of these situations, classical textbook results on weak
convergence theory suffice for the problem at hand. However, there are quite
some scenarios where stronger results are needed in order to establish an
asymptotic normal approximation uniformly over a family of probability
measures. In this note we collect some results in this direction. We restrict
ourselves to weak convergence in $\mathbb R^d$ with continuous limit measures.
author:
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory.
arXiv:190309864. 2019.
apa: Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak
convergence theory. ArXiv:1903.09864.
bibtex: '@article{Bengs_Holzmann_2019, title={Uniform approximation in classical
weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and
Holzmann, Hajo}, year={2019} }'
chicago: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak
Convergence Theory.” ArXiv:1903.09864, 2019.
ieee: V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence
theory,” arXiv:1903.09864. 2019.
mla: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak
Convergence Theory.” ArXiv:1903.09864, 2019.
short: V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).
date_created: 2020-08-17T12:10:55Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
publication: arXiv:1903.09864
status: public
title: Uniform approximation in classical weak convergence theory
type: preprint
user_id: '76599'
year: '2019'
...
---
_id: '8868'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Alexander
full_name: Hetzer, Alexander
id: '38209'
last_name: Hetzer
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning
for Multi-Label Classification. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated
Machine Learning for Multi-Label Classification. Presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated
Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik
and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer.
“Towards Automated Machine Learning for Multi-Label Classification,” 2019.
ieee: M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine
Learning for Multi-Label Classification,” presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany, 2019.
mla: Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label
Classification. 2019.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.'
conference:
end_date: 2019-03-20
location: Bayreuth, Germany
name: European Conference on Data Analytics (ECDA)
start_date: 2019-03-18
date_created: 2019-04-10T07:17:55Z
date_updated: 2022-01-06T07:04:04Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2019-04-10T07:17:17Z
date_updated: 2019-04-10T07:17:17Z
file_id: '8870'
file_name: Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf
file_size: '74484'
relation: main_file
success: 1
file_date_updated: 2019-04-10T07:17:17Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Towards Automated Machine Learning for Multi-Label Classification
type: conference_abstract
user_id: '49109'
year: '2019'
...
---
_id: '10578'
author:
- first_name: V. K.
full_name: Tagne, V. K.
last_name: Tagne
- first_name: S.
full_name: Fotso, S.
last_name: Fotso
- first_name: 'L. A. '
full_name: 'Fono, L. A. '
last_name: Fono
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows
and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
apa: Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice
Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics
and Natural Computation, 15(2), 191–213.
bibtex: '@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated
by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New
Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono,
L. A. and Hüllermeier, Eyke}, year={2019}, pages={191–213} }'
chicago: 'Tagne, V. K., S. Fotso, L. A. Fono, and Eyke Hüllermeier. “Choice Functions
Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural
Computation 15, no. 2 (2019): 191–213.'
ieee: V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated
by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation,
vol. 15, no. 2, pp. 191–213, 2019.
mla: Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce
Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019,
pp. 191–213.
short: V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural
Computation 15 (2019) 191–213.
date_created: 2019-07-08T15:34:03Z
date_updated: 2022-01-06T06:50:45Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
intvolume: ' 15'
issue: '2'
language:
- iso: eng
page: 191-213
publication: New Mathematics and Natural Computation
status: public
title: Choice Functions Generated by Mallows and Plackett–Luce Relations
type: journal_article
user_id: '315'
volume: 15
year: '2019'
...
---
_id: '15001'
author:
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: Christian
full_name: Borgelt, Christian
last_name: Borgelt
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Rudolf
full_name: Kruse, Rudolf
last_name: Kruse
citation:
ama: 'Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From
Statistical Foundations to Machine Learning. IEEE Computational Intelligence
Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642'
apa: 'Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets
in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational
Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642'
bibtex: '@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data
Analysis: From Statistical Foundations to Machine Learning}, DOI={10.1109/mci.2018.2881642},
journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt,
Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44}
}'
chicago: 'Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy
Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE
Computational Intelligence Magazine, 2019, 31–44. https://doi.org/10.1109/mci.2018.2881642.'
ieee: 'I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis:
From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence
Magazine, pp. 31–44, 2019.'
mla: 'Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations
to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, pp.
31–44, doi:10.1109/mci.2018.2881642.'
short: I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence
Magazine (2019) 31–44.
date_created: 2019-11-15T10:11:37Z
date_updated: 2022-01-06T06:52:13Z
department:
- _id: '34'
- _id: '355'
doi: 10.1109/mci.2018.2881642
language:
- iso: eng
page: 31-44
publication: IEEE Computational Intelligence Magazine
publication_identifier:
issn:
- 1556-603X
- 1556-6048
publication_status: published
status: public
title: 'Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning'
type: journal_article
user_id: '315'
year: '2019'
...
---
_id: '15002'
abstract:
- lang: eng
text: Many problem settings in machine learning are concerned with the simultaneous
prediction of multiple target variables of diverse type. Amongst others, such
problem settings arise in multivariate regression, multi-label classification,
multi-task learning, dyadic prediction, zero-shot learning, network inference,
and matrix completion. These subfields of machine learning are typically studied
in isolation, without highlighting or exploring important relationships. In this
paper, we present a unifying view on what we call multi-target prediction (MTP)
problems and methods. First, we formally discuss commonalities and differences
between existing MTP problems. To this end, we introduce a general framework that
covers the above subfields as special cases. As a second contribution, we provide
a structured overview of MTP methods. This is accomplished by identifying a number
of key properties, which distinguish such methods and determine their suitability
for different types of problems. Finally, we also discuss a few challenges for
future research.
author:
- first_name: Willem
full_name: Waegeman, Willem
last_name: Waegeman
- first_name: Krzysztof
full_name: Dembczynski, Krzysztof
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying
view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324.
doi:10.1007/s10618-018-0595-5'
apa: 'Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target
prediction: a unifying view on problems and methods. Data Mining and Knowledge
Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5'
bibtex: '@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction:
a unifying view on problems and methods}, volume={33}, DOI={10.1007/s10618-018-0595-5},
number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem
and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324}
}'
chicago: 'Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target
Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge
Discovery 33, no. 2 (2019): 293–324. https://doi.org/10.1007/s10618-018-0595-5.'
ieee: 'W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction:
a unifying view on problems and methods,” Data Mining and Knowledge Discovery,
vol. 33, no. 2, pp. 293–324, 2019.'
mla: 'Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems
and Methods.” Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019,
pp. 293–324, doi:10.1007/s10618-018-0595-5.'
short: W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery
33 (2019) 293–324.
date_created: 2019-11-15T10:16:34Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
doi: 10.1007/s10618-018-0595-5
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:43:39Z
date_updated: 2020-02-28T12:45:26Z
file_id: '16155'
file_name: multi-target-prediction.pdf
file_size: 837808
relation: main_file
file_date_updated: 2020-02-28T12:45:26Z
has_accepted_license: '1'
intvolume: ' 33'
issue: '2'
language:
- iso: eng
oa: '1'
page: 293-324
publication: Data Mining and Knowledge Discovery
publication_identifier:
issn:
- 1573-756X
status: public
title: 'Multi-target prediction: a unifying view on problems and methods'
type: journal_article
user_id: '315'
volume: 33
year: '2019'
...
---
_id: '15003'
author:
- first_name: Thomas
full_name: Mortier, Thomas
last_name: Mortier
- first_name: Marek
full_name: Wydmuch, Marek
last_name: Wydmuch
- first_name: Krzysztof
full_name: Dembczynski, Krzysztof
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Willem
full_name: Waegeman, Willem
last_name: Waegeman
citation:
ama: 'Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued
Prediction in Multi-Class Classification. In: Proceedings of the 31st Benelux
Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch
Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8,
2019. ; 2019.'
apa: Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman,
W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings
of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the
28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels,
Belgium, November 6-8, 2019.
bibtex: '@inproceedings{Mortier_Wydmuch_Dembczynski_Hüllermeier_Waegeman_2019, title={Set-Valued
Prediction in Multi-Class Classification}, booktitle={Proceedings of the 31st
Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian
Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November
6-8, 2019}, author={Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof
and Hüllermeier, Eyke and Waegeman, Willem}, year={2019} }'
chicago: Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier,
and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” In
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.
ieee: T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued
Prediction in Multi-Class Classification,” in Proceedings of the 31st Benelux
Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch
Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8,
2019, 2019.
mla: Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.”
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.
short: 'T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in:
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.'
date_created: 2019-11-15T10:20:55Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
language:
- iso: eng
publication: Proceedings of the 31st Benelux Conference on Artificial Intelligence
{(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn
2019), Brussels, Belgium, November 6-8, 2019
status: public
title: Set-Valued Prediction in Multi-Class Classification
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15004'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
id: '59547'
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning
to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22'
apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based
Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Feature
Selection for Analogy-Based Learning to Rank}, DOI={10.1007/978-3-030-33778-0_22},
booktitle={Discovery Science}, author={Ahmadi Fahandar, Mohsen and Hüllermeier,
Eyke}, year={2019} }'
chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based
Learning to Rank.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_22.
ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based
Learning to Rank,” in Discovery Science, Cham, 2019.
mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based
Learning to Rank.” Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_22.
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.'
date_created: 2019-11-15T10:24:45Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-33778-0_22
language:
- iso: eng
place: Cham
publication: Discovery Science
publication_identifier:
isbn:
- '9783030337773'
- '9783030337780'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Feature Selection for Analogy-Based Learning to Rank
type: book_chapter
user_id: '315'
year: '2019'
...
---
_id: '15005'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
id: '59547'
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels.
In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3'
apa: 'Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference
Learning with Kernels. In KI 2019: Advances in Artificial Intelligence.
Cham. https://doi.org/10.1007/978-3-030-30179-8_3'
bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Analogy-Based
Preference Learning with Kernels}, DOI={10.1007/978-3-030-30179-8_3},
booktitle={KI 2019: Advances in Artificial Intelligence}, author={Ahmadi Fahandar,
Mohsen and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference
Learning with Kernels.” In KI 2019: Advances in Artificial Intelligence.
Cham, 2019. https://doi.org/10.1007/978-3-030-30179-8_3.'
ieee: 'M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning
with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.'
mla: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning
with Kernels.” KI 2019: Advances in Artificial Intelligence, 2019, doi:10.1007/978-3-030-30179-8_3.'
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial
Intelligence, Cham, 2019.'
date_created: 2019-11-15T10:30:10Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-30179-8_3
language:
- iso: eng
place: Cham
publication: 'KI 2019: Advances in Artificial Intelligence'
publication_identifier:
isbn:
- '9783030301781'
- '9783030301798'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Analogy-Based Preference Learning with Kernels
type: book_chapter
user_id: '315'
year: '2019'
...
---
_id: '15006'
author:
- first_name: Vu-Linh
full_name: Nguyen, Vu-Linh
last_name: Nguyen
- first_name: Sébastien
full_name: Destercke, Sébastien
last_name: Destercke
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In:
Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7'
apa: Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty
Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
bibtex: '@inbook{Nguyen_Destercke_Hüllermeier_2019, place={Cham}, title={Epistemic
Uncertainty Sampling}, DOI={10.1007/978-3-030-33778-0_7},
booktitle={Discovery Science}, author={Nguyen, Vu-Linh and Destercke, Sébastien
and Hüllermeier, Eyke}, year={2019} }'
chicago: Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic
Uncertainty Sampling.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_7.
ieee: V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,”
in Discovery Science, Cham, 2019.
mla: Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.” Discovery Science,
2019, doi:10.1007/978-3-030-33778-0_7.
short: 'V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham,
2019.'
date_created: 2019-11-15T10:35:08Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-33778-0_7
language:
- iso: eng
place: Cham
publication: Discovery Science
publication_identifier:
isbn:
- '9783030337773'
- '9783030337780'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Epistemic Uncertainty Sampling
type: book_chapter
user_id: '49109'
year: '2019'
...
---
_id: '15007'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017'
apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101).
https://doi.org/10.1016/j.jmva.2019.02.017'
bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate:
Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017},
booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings
of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier,
Eyke}, year={2019} }'
chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101),
2019. https://doi.org/10.1016/j.jmva.2019.02.017.'
ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101), 2019.'
mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the
Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference
on Machine Learning (Proceedings of Machine Learning Research, 101), 2019,
doi:10.1016/j.jmva.2019.02.017.'
short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine
Learning (Proceedings of Machine Learning Research, 101), 2019.'
date_created: 2019-11-15T10:43:26Z
date_updated: 2022-01-06T06:52:14Z
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- _id: '355'
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doi: 10.1016/j.jmva.2019.02.017
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creator: lettmann
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file_size: 2331320
relation: main_file
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language:
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oa: '1'
project:
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name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of
Machine Learning Research, 101)
publication_status: published
status: public
title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for
OWA'
type: conference
user_id: '477'
year: '2019'
...
---
_id: '15009'
author:
- first_name: Nico
full_name: Epple, Nico
last_name: Epple
- first_name: Simone
full_name: Dari, Simone
last_name: Dari
- first_name: Ludwig
full_name: Drees, Ludwig
last_name: Drees
- first_name: Valentin
full_name: Protschky, Valentin
last_name: Protschky
- first_name: Andreas
full_name: Riener, Andreas
last_name: Riener
citation:
ama: 'Epple N, Dari S, Drees L, Protschky V, Riener A. Influence of Cruise Control
on Driver Guidance - a Comparison between System Generations and Countries. In:
2019 IEEE Intelligent Vehicles Symposium (IV). ; 2019. doi:10.1109/ivs.2019.8814100'
apa: Epple, N., Dari, S., Drees, L., Protschky, V., & Riener, A. (2019). Influence
of Cruise Control on Driver Guidance - a Comparison between System Generations
and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2019.8814100
bibtex: '@inproceedings{Epple_Dari_Drees_Protschky_Riener_2019, title={Influence
of Cruise Control on Driver Guidance - a Comparison between System Generations
and Countries}, DOI={10.1109/ivs.2019.8814100},
booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, author={Epple, Nico
and Dari, Simone and Drees, Ludwig and Protschky, Valentin and Riener, Andreas},
year={2019} }'
chicago: Epple, Nico, Simone Dari, Ludwig Drees, Valentin Protschky, and Andreas
Riener. “Influence of Cruise Control on Driver Guidance - a Comparison between
System Generations and Countries.” In 2019 IEEE Intelligent Vehicles Symposium
(IV), 2019. https://doi.org/10.1109/ivs.2019.8814100.
ieee: N. Epple, S. Dari, L. Drees, V. Protschky, and A. Riener, “Influence of Cruise
Control on Driver Guidance - a Comparison between System Generations and Countries,”
in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.
mla: Epple, Nico, et al. “Influence of Cruise Control on Driver Guidance - a Comparison
between System Generations and Countries.” 2019 IEEE Intelligent Vehicles Symposium
(IV), 2019, doi:10.1109/ivs.2019.8814100.
short: 'N. Epple, S. Dari, L. Drees, V. Protschky, A. Riener, in: 2019 IEEE Intelligent
Vehicles Symposium (IV), 2019.'
date_created: 2019-11-15T10:54:04Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1109/ivs.2019.8814100
language:
- iso: eng
publication: 2019 IEEE Intelligent Vehicles Symposium (IV)
publication_identifier:
isbn:
- '9781728105604'
publication_status: published
status: public
title: Influence of Cruise Control on Driver Guidance - a Comparison between System
Generations and Countries
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15011'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation:
From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut
R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28.
- 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.'
apa: 'Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection
as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann,
E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational
Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT
Scientific Publishing, Karlsruhe.'
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection
as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings
- 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019},
publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and
Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier,
Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }'
chicago: 'Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm
Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In
Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29.
November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut,
135–46. KIT Scientific Publishing, Karlsruhe, 2019.'
ieee: 'A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation:
From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop
Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019,
pp. 135–146.'
mla: 'Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative
Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence,
Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific
Publishing, Karlsruhe, 2019, pp. 135–46.'
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier,
R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund,
28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.'
conference:
end_date: 2019-11-29
location: Dortmund
name: 29. Workshop Computational Intelligence
start_date: 2019-11-28
date_created: 2019-11-15T13:29:25Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Frank
full_name: Hoffmann, Frank
last_name: Hoffmann
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Ralf
full_name: Mikut, Ralf
last_name: Mikut
file:
- access_level: open_access
content_type: application/pdf
creator: ahetzer
date_created: 2020-05-25T08:01:31Z
date_updated: 2020-05-25T08:01:31Z
file_id: '17060'
file_name: ci_workshop_tornede.pdf
file_size: 468825
relation: main_file
file_date_updated: 2020-05-25T08:01:31Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 135-146
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28.
- 29. November 2019
publication_identifier:
isbn:
- 978-3-7315-0979-0
publication_status: published
publisher: KIT Scientific Publishing, Karlsruhe
status: public
title: 'Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad
Ranking'
type: conference
user_id: '38209'
year: '2019'
...
---
_id: '15013'
author:
- first_name: Klaus
full_name: Brinker, Klaus
last_name: Brinker
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification.
In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge
Discovery in Databases. Würzburg, Germany; 2019.'
apa: Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to
Multiclass Classification. In Proceedings ECML/PKDD, European Conference on
Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.
bibtex: '@inproceedings{Brinker_Hüllermeier_2019, place={Würzburg, Germany}, title={A
Reduction of Label Ranking to Multiclass Classification}, booktitle={Proceedings
ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in
Databases}, author={Brinker, Klaus and Hüllermeier, Eyke}, year={2019} }'
chicago: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to
Multiclass Classification.” In Proceedings ECML/PKDD, European Conference on
Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany,
2019.
ieee: K. Brinker and E. Hüllermeier, “A Reduction of Label Ranking to Multiclass
Classification,” in Proceedings ECML/PKDD, European Conference on Machine Learning
and Knowledge Discovery in Databases, 2019.
mla: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass
Classification.” Proceedings ECML/PKDD, European Conference on Machine Learning
and Knowledge Discovery in Databases, 2019.
short: 'K. Brinker, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference
on Machine Learning and Knowledge Discovery in Databases, Würzburg, Germany, 2019.'
date_created: 2019-11-18T07:26:43Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
language:
- iso: eng
place: Würzburg, Germany
publication: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge
Discovery in Databases
status: public
title: A Reduction of Label Ranking to Multiclass Classification
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15014'
author:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: Sebastian
full_name: Diestercke, Sebastian
last_name: Diestercke
citation:
ama: 'Hüllermeier E, Couso I, Diestercke S. Learning from Imprecise Data: Adjustments
of Optimistic and Pessimistic Variants. In: Proceedings SUM 2019, International
Conference on Scalable Uncertainty Management. ; 2019.'
apa: 'Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise
Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM
2019, International Conference on Scalable Uncertainty Management.'
bibtex: '@inproceedings{Hüllermeier_Couso_Diestercke_2019, title={Learning from
Imprecise Data: Adjustments of Optimistic and Pessimistic Variants}, booktitle={Proceedings
SUM 2019, International Conference on Scalable Uncertainty Management}, author={Hüllermeier,
Eyke and Couso, Ines and Diestercke, Sebastian}, year={2019} }'
chicago: 'Hüllermeier, Eyke, Ines Couso, and Sebastian Diestercke. “Learning from
Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” In Proceedings
SUM 2019, International Conference on Scalable Uncertainty Management, 2019.'
ieee: 'E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data:
Adjustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019,
International Conference on Scalable Uncertainty Management, 2019.'
mla: 'Hüllermeier, Eyke, et al. “Learning from Imprecise Data: Adjustments of Optimistic
and Pessimistic Variants.” Proceedings SUM 2019, International Conference on
Scalable Uncertainty Management, 2019.'
short: 'E. Hüllermeier, I. Couso, S. Diestercke, in: Proceedings SUM 2019, International
Conference on Scalable Uncertainty Management, 2019.'
date_created: 2019-11-18T07:38:13Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
language:
- iso: eng
publication: Proceedings SUM 2019, International Conference on Scalable Uncertainty
Management
status: public
title: 'Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants'
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15015'
author:
- first_name: Sascha
full_name: Henzgen, Sascha
last_name: Henzgen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge
Discovery from Data. 2019:1-36. doi:10.1145/3363572
apa: Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions
on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572
bibtex: '@article{Henzgen_Hüllermeier_2019, title={Mining Rank Data}, DOI={10.1145/3363572},
journal={ACM Transactions on Knowledge Discovery from Data}, author={Henzgen,
Sascha and Hüllermeier, Eyke}, year={2019}, pages={1–36} }'
chicago: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions
on Knowledge Discovery from Data, 2019, 1–36. https://doi.org/10.1145/3363572.
ieee: S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on
Knowledge Discovery from Data, pp. 1–36, 2019.
mla: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions
on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572.
short: S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from
Data (2019) 1–36.
date_created: 2019-11-18T07:40:27Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
doi: 10.1145/3363572
language:
- iso: eng
page: 1-36
publication: ACM Transactions on Knowledge Discovery from Data
publication_identifier:
issn:
- 1556-4681
publication_status: published
status: public
title: Mining Rank Data
type: journal_article
user_id: '315'
year: '2019'
...
---
_id: '14027'
author:
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Matthias
full_name: Eulert, Matthias
last_name: Eulert
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve
in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312.
doi:10.1016/j.jmva.2019.02.017
apa: Bengs, V., Eulert, M., & Holzmann, H. (2019). Asymptotic confidence sets
for the jump curve in bivariate regression problems. Journal of Multivariate
Analysis, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017
bibtex: '@article{Bengs_Eulert_Holzmann_2019, title={Asymptotic confidence sets
for the jump curve in bivariate regression problems}, DOI={10.1016/j.jmva.2019.02.017},
journal={Journal of Multivariate Analysis}, author={Bengs, Viktor and Eulert,
Matthias and Holzmann, Hajo}, year={2019}, pages={291–312} }'
chicago: Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence
Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate
Analysis, 2019, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017.
ieee: V. Bengs, M. Eulert, and H. Holzmann, “Asymptotic confidence sets for the
jump curve in bivariate regression problems,” Journal of Multivariate Analysis,
pp. 291–312, 2019.
mla: Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate
Regression Problems.” Journal of Multivariate Analysis, 2019, pp. 291–312,
doi:10.1016/j.jmva.2019.02.017.
short: V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019)
291–312.
date_created: 2019-10-30T14:22:57Z
date_updated: 2022-01-06T06:51:52Z
department:
- _id: '34'
- _id: '355'
doi: 10.1016/j.jmva.2019.02.017
language:
- iso: eng
page: 291-312
publication: Journal of Multivariate Analysis
publication_identifier:
issn:
- 0047-259X
publication_status: published
status: public
title: Asymptotic confidence sets for the jump curve in bivariate regression problems
type: journal_article
user_id: '76599'
year: '2019'
...
---
_id: '14028'
author:
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic
Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555
apa: Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation.
Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555
bibtex: '@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink
estimation}, DOI={10.1214/19-ejs1555},
journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann,
Hajo}, year={2019}, pages={1523–1579} }'
chicago: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”
Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555.
ieee: V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,”
Electronic Journal of Statistics, pp. 1523–1579, 2019.
mla: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”
Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555.
short: V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.
date_created: 2019-10-30T14:25:16Z
date_updated: 2022-01-06T06:51:52Z
department:
- _id: '34'
- _id: '355'
doi: 10.1214/19-ejs1555
language:
- iso: eng
page: 1523-1579
publication: Electronic Journal of Statistics
publication_identifier:
issn:
- 1935-7524
publication_status: published
status: public
title: Adaptive confidence sets for kink estimation
type: journal_article
user_id: '76599'
year: '2019'
...
---
_id: '13132'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine
Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik
Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft
für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.'
apa: 'Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated
to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für
Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft
für Informatik e.V.'
bibtex: '@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From
Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre
Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft
für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede,
Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre
Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft
für Informatik e.V., 2019.'
ieee: 'F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to
On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für
Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.'
mla: 'Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK
2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft,
Gesellschaft für Informatik e.V., 2019, pp. 273–74.'
short: 'F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50
Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft
für Informatik e.V., Bonn, 2019, pp. 273–274.'
conference:
end_date: 2019-09-26
location: Kassel
name: Informatik 2019
start_date: 2019-09-23
date_created: 2019-09-04T08:44:46Z
date_updated: 2022-01-06T06:51:28Z
department:
- _id: '355'
language:
- iso: eng
page: ' 273-274 '
place: Bonn
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: 'INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für
Gesellschaft'
publisher: Gesellschaft für Informatik e.V.
series_title: INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für
Informatik
status: public
title: From Automated to On-The-Fly Machine Learning
type: conference_abstract
user_id: '38209'
year: '2019'
...
---
_id: '10232'
abstract:
- lang: eng
text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn,
and more recently ML-Plan, have shown impressive results for the tasks of single-label
classification and regression. Yet, there is only little work on other types of
machine learning problems so far. In particular, there is almost no work on automating
the engineering of machine learning solutions for multi-label classification (MLC).
We show how the scope of ML-Plan, an AutoML-tool for multi-class classification,
can be extended towards MLC using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, nesting other multi-label classifiers for meta algorithms
and single-label classifiers provided by WEKA as base learners. In our evaluation,
we find that the proposed approach yields strong results and performs significantly
better than a set of baselines we compare with.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification
Extending ML-Plan. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating
Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop
on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label
Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix
and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier.
“Automating Multi-Label Classification Extending ML-Plan,” 2019.
ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label
Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated
Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending
ML-Plan. 2019.
short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.'
conference:
end_date: 2019-06-15
location: Long Beach, CA, USA
name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019)
start_date: 2019-06-09
date_created: 2019-06-11T21:33:06Z
date_updated: 2022-01-06T06:50:33Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2019-09-10T08:19:01Z
date_updated: 2019-09-10T08:20:44Z
file_id: '13177'
file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
file_size: 388191
relation: main_file
file_date_updated: 2019-09-10T08:20:44Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Automating Multi-Label Classification Extending ML-Plan
type: conference
user_id: '33176'
year: '2019'
...
---
_id: '20243'
author:
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
- first_name: Giuseppe
full_name: Leonardi, Giuseppe
last_name: Leonardi
- first_name: Iris
full_name: Nomikou, Iris
last_name: Nomikou
- first_name: Joanna
full_name: Rączaszek-Leonardi, Joanna
last_name: Rączaszek-Leonardi
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal
Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE
Transactions on Cognitive and Developmental Systems. Published online 2019.
doi:10.1109/TCDS.2019.2892991'
apa: 'Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier,
E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and
Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems.
https://doi.org/10.1109/TCDS.2019.2892991'
bibtex: '@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019,
title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel
Approaches}, DOI={10.1109/TCDS.2019.2892991},
journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing,
Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna
and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi,
and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges,
and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems,
2019. https://doi.org/10.1109/TCDS.2019.2892991.'
ieee: 'K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier,
“Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,”
IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.'
mla: 'Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological
Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental
Systems, 2019, doi:10.1109/TCDS.2019.2892991.'
short: K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier,
IEEE Transactions on Cognitive and Developmental Systems (2019).
date_created: 2020-11-02T13:25:49Z
date_updated: 2023-02-01T12:39:19Z
department:
- _id: '749'
- _id: '355'
doi: 10.1109/TCDS.2019.2892991
language:
- iso: eng
publication: IEEE Transactions on Cognitive and Developmental Systems
status: public
title: 'Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel
Approaches'
type: journal_article
user_id: '14931'
year: '2019'
...