---
_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'
...