---
_id: '21636'
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Lienen J, Hüllermeier E. Instance weighting through data imprecisiation. International
Journal of Approximate Reasoning. 2021.
apa: Lienen, J., & Hüllermeier, E. (2021). Instance weighting through data imprecisiation.
International Journal of Approximate Reasoning.
bibtex: '@article{Lienen_Hüllermeier_2021, title={Instance weighting through data
imprecisiation}, journal={International Journal of Approximate Reasoning}, publisher={Elsevier},
author={Lienen, Julian and Hüllermeier, Eyke}, year={2021} }'
chicago: Lienen, Julian, and Eyke Hüllermeier. “Instance Weighting through Data
Imprecisiation.” International Journal of Approximate Reasoning, 2021.
ieee: J. Lienen and E. Hüllermeier, “Instance weighting through data imprecisiation,”
International Journal of Approximate Reasoning, 2021.
mla: Lienen, Julian, and Eyke Hüllermeier. “Instance Weighting through Data Imprecisiation.”
International Journal of Approximate Reasoning, Elsevier, 2021.
short: J. Lienen, E. Hüllermeier, International Journal of Approximate Reasoning
(2021).
date_created: 2021-04-20T06:48:18Z
date_updated: 2022-01-06T06:55:08Z
language:
- iso: eng
main_file_link:
- url: https://www.sciencedirect.com/science/article/pii/S0888613X21000463
publication: International Journal of Approximate Reasoning
publisher: Elsevier
status: public
title: Instance weighting through data imprecisiation
type: journal_article
user_id: '44040'
year: '2021'
...
---
_id: '21637'
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Lienen J, Hüllermeier E. From Label Smoothing to Label Relaxation. In: Proceedings
of the 35th AAAI Conference on Artificial Intelligence, AAAI. Vol 35. AAAI
Press; 2021:8583-8591.'
apa: 'Lienen, J., & Hüllermeier, E. (2021). From Label Smoothing to Label Relaxation.
In Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI
(Vol. 35, pp. 8583–8591). Online: AAAI Press.'
bibtex: '@inproceedings{Lienen_Hüllermeier_2021, title={From Label Smoothing to
Label Relaxation}, volume={35}, number={10}, booktitle={Proceedings of the 35th
AAAI Conference on Artificial Intelligence, AAAI}, publisher={AAAI Press}, author={Lienen,
Julian and Hüllermeier, Eyke}, year={2021}, pages={8583–8591} }'
chicago: Lienen, Julian, and Eyke Hüllermeier. “From Label Smoothing to Label Relaxation.”
In Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI,
35:8583–91. AAAI Press, 2021.
ieee: J. Lienen and E. Hüllermeier, “From Label Smoothing to Label Relaxation,”
in Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI,
Online, 2021, vol. 35, no. 10, pp. 8583–8591.
mla: Lienen, Julian, and Eyke Hüllermeier. “From Label Smoothing to Label Relaxation.”
Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI,
vol. 35, no. 10, AAAI Press, 2021, pp. 8583–91.
short: 'J. Lienen, E. Hüllermeier, in: Proceedings of the 35th AAAI Conference on
Artificial Intelligence, AAAI, AAAI Press, 2021, pp. 8583–8591.'
conference:
end_date: 2021-02-09
location: Online
name: 35th AAAI Conference on Artificial Intelligence, AAAI
start_date: 2021-02-02
date_created: 2021-04-20T06:50:43Z
date_updated: 2022-01-06T06:55:08Z
intvolume: ' 35'
issue: '10'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ojs.aaai.org/index.php/AAAI/article/view/17041
oa: '1'
page: 8583-8591
publication: Proceedings of the 35th AAAI Conference on Artificial Intelligence, AAAI
publisher: AAAI Press
status: public
title: From Label Smoothing to Label Relaxation
type: conference
user_id: '44040'
volume: 35
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: '22280'
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Ralph
full_name: Ewerth, Ralph
last_name: Ewerth
- first_name: Nils
full_name: Nommensen, Nils
last_name: Nommensen
citation:
ama: 'Lienen J, Hüllermeier E, Ewerth R, Nommensen N. Monocular Depth Estimation
via Listwise Ranking using the Plackett-Luce Model. In: Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR. ; 2021:14595-14604.'
apa: Lienen, J., Hüllermeier, E., Ewerth, R., & Nommensen, N. (2021). Monocular
Depth Estimation via Listwise Ranking using the Plackett-Luce Model. Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR,
14595–14604.
bibtex: '@inproceedings{Lienen_Hüllermeier_Ewerth_Nommensen_2021, title={Monocular
Depth Estimation via Listwise Ranking using the Plackett-Luce Model}, booktitle={Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR},
author={Lienen, Julian and Hüllermeier, Eyke and Ewerth, Ralph and Nommensen,
Nils}, year={2021}, pages={14595–14604} }'
chicago: Lienen, Julian, Eyke Hüllermeier, Ralph Ewerth, and Nils Nommensen. “Monocular
Depth Estimation via Listwise Ranking Using the Plackett-Luce Model.” In Proceedings
of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR,
14595–604, 2021.
ieee: J. Lienen, E. Hüllermeier, R. Ewerth, and N. Nommensen, “Monocular Depth Estimation
via Listwise Ranking using the Plackett-Luce Model,” in Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, Online,
2021, pp. 14595–14604.
mla: Lienen, Julian, et al. “Monocular Depth Estimation via Listwise Ranking Using
the Plackett-Luce Model.” Proceedings of the IEEE/CVF Conference on Computer
Vision and Pattern Recognition, CVPR, 2021, pp. 14595–604.
short: 'J. Lienen, E. Hüllermeier, R. Ewerth, N. Nommensen, in: Proceedings of the
IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR, 2021, pp.
14595–14604.'
conference:
end_date: 2021-06-25
location: Online
name: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR
start_date: 2021-06-19
date_created: 2021-06-02T10:35:40Z
date_updated: 2022-01-06T06:55:29Z
language:
- iso: eng
page: 14595-14604
publication: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern
Recognition, CVPR
status: public
title: Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce Model
type: conference
user_id: '44040'
year: '2021'
...
---
_id: '22509'
abstract:
- lang: eng
text: Self-training is an effective approach to semi-supervised learning. The key
idea is to let the learner itself iteratively generate "pseudo-supervision" for
unlabeled instances based on its current hypothesis. In combination with consistency
regularization, pseudo-labeling has shown promising performance in various domains,
for example in computer vision. To account for the hypothetical nature of the
pseudo-labels, these are commonly provided in the form of probability distributions.
Still, one may argue that even a probability distribution represents an excessive
level of informedness, as it suggests that the learner precisely knows the ground-truth
conditional probabilities. In our approach, we therefore allow the learner to
label instances in the form of credal sets, that is, sets of (candidate) probability
distributions. Thanks to this increased expressiveness, the learner is able to
represent uncertainty and a lack of knowledge in a more flexible and more faithful
manner. To learn from weakly labeled data of that kind, we leverage methods that
have recently been proposed in the realm of so-called superset learning. In an
exhaustive empirical evaluation, we compare our methodology to state-of-the-art
self-supervision approaches, showing competitive to superior performance especially
in low-label scenarios incorporating a high degree of uncertainty.
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Lienen J, Hüllermeier E. Credal Self-Supervised Learning. arXiv:210611853.
2021.
apa: Lienen, J., & Hüllermeier, E. (2021). Credal Self-Supervised Learning.
ArXiv:2106.11853.
bibtex: '@article{Lienen_Hüllermeier_2021, title={Credal Self-Supervised Learning},
journal={arXiv:2106.11853}, author={Lienen, Julian and Hüllermeier, Eyke}, year={2021}
}'
chicago: Lienen, Julian, and Eyke Hüllermeier. “Credal Self-Supervised Learning.”
ArXiv:2106.11853, 2021.
ieee: J. Lienen and E. Hüllermeier, “Credal Self-Supervised Learning,” arXiv:2106.11853.
2021.
mla: Lienen, Julian, and Eyke Hüllermeier. “Credal Self-Supervised Learning.” ArXiv:2106.11853,
2021.
short: J. Lienen, E. Hüllermeier, ArXiv:2106.11853 (2021).
date_created: 2021-06-23T07:24:38Z
date_updated: 2022-01-06T06:55:35Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/2106.11853.pdf
oa: '1'
publication: arXiv:2106.11853
status: public
title: Credal Self-Supervised Learning
type: preprint
user_id: '44040'
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: '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: '30866'
abstract:
- lang: eng
text: "Automated machine learning (AutoML) strives for the automatic configuration\r\nof
machine learning algorithms and their composition into an overall (software)\r\nsolution
- a machine learning pipeline - tailored to the learning task\r\n(dataset) at
hand. Over the last decade, AutoML has developed into an\r\nindependent research
field with hundreds of contributions. While AutoML offers\r\nmany prospects, it
is also known to be quite resource-intensive, which is one\r\nof its major points
of criticism. The primary cause for a high resource\r\nconsumption is that many
approaches rely on the (costly) evaluation of many\r\nmachine learning pipelines
while searching for good candidates. This problem is\r\namplified in the context
of research on AutoML methods, due to large scale\r\nexperiments conducted with
many datasets and approaches, each of them being run\r\nwith several repetitions
to rule out random effects. In the spirit of recent\r\nwork on Green AI, this
paper is written in an attempt to raise the awareness of\r\nAutoML researchers
for the problem and to elaborate on possible remedies. To\r\nthis end, we identify
four categories of actions the community may take towards\r\nmore sustainable
research on AutoML, i.e. Green AutoML: design of AutoML\r\nsystems, benchmarking,
transparency and research incentives."
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: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- 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, Hanselle JM, Wever MD, Mohr F, Hüllermeier E. Towards
Green Automated Machine Learning: Status Quo and Future Directions. arXiv:211105850.
Published online 2021.'
apa: 'Tornede, T., Tornede, A., Hanselle, J. M., Wever, M. D., Mohr, F., & Hüllermeier,
E. (2021). Towards Green Automated Machine Learning: Status Quo and Future Directions.
In arXiv:2111.05850.'
bibtex: '@article{Tornede_Tornede_Hanselle_Wever_Mohr_Hüllermeier_2021, title={Towards
Green Automated Machine Learning: Status Quo and Future Directions}, journal={arXiv:2111.05850},
author={Tornede, Tanja and Tornede, Alexander and Hanselle, Jonas Manuel and Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2021} }'
chicago: 'Tornede, Tanja, Alexander Tornede, Jonas Manuel Hanselle, Marcel Dominik
Wever, Felix Mohr, and Eyke Hüllermeier. “Towards Green Automated Machine Learning:
Status Quo and Future Directions.” ArXiv:2111.05850, 2021.'
ieee: 'T. Tornede, A. Tornede, J. M. Hanselle, M. D. Wever, F. Mohr, and E. Hüllermeier,
“Towards Green Automated Machine Learning: Status Quo and Future Directions,”
arXiv:2111.05850. 2021.'
mla: 'Tornede, Tanja, et al. “Towards Green Automated Machine Learning: Status Quo
and Future Directions.” ArXiv:2111.05850, 2021.'
short: T. Tornede, A. Tornede, J.M. Hanselle, M.D. Wever, F. Mohr, E. Hüllermeier,
ArXiv:2111.05850 (2021).
date_created: 2022-04-12T11:57:15Z
date_updated: 2022-04-12T12:01:23Z
department:
- _id: '34'
- _id: '7'
- _id: '26'
external_id:
arxiv:
- '2111.05850'
language:
- iso: eng
project:
- _id: '1'
name: 'SFB 901: SFB 901'
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
name: 'SFB 901 - B2: SFB 901 - Subproject B2'
publication: arXiv:2111.05850
status: public
title: 'Towards Green Automated Machine Learning: Status Quo and Future Directions'
type: preprint
user_id: '38209'
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: '29292'
author:
- first_name: Robert
full_name: Feldhans, Robert
last_name: Feldhans
- first_name: Adrian
full_name: Wilke, Adrian
id: '9101'
last_name: Wilke
orcid: 0000-0002-6575-807X
- first_name: Stefan
full_name: Heindorf, Stefan
id: '11871'
last_name: Heindorf
orcid: 0000-0002-4525-6865
- first_name: Mohammad Hossein
full_name: Shaker, Mohammad Hossein
last_name: Shaker
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Feldhans R, Wilke A, Heindorf S, et al. Drift Detection in Text Data with
Document Embeddings. In: Intelligent Data Engineering and Automated Learning
– IDEAL 2021. Springer International Publishing; 2021. doi:10.1007/978-3-030-91608-4_11'
apa: Feldhans, R., Wilke, A., Heindorf, S., Shaker, M. H., Hammer, B., Ngonga Ngomo,
A.-C., & Hüllermeier, E. (2021). Drift Detection in Text Data with Document
Embeddings. In Intelligent Data Engineering and Automated Learning – IDEAL
2021. Springer International Publishing. https://doi.org/10.1007/978-3-030-91608-4_11
bibtex: '@inbook{Feldhans_Wilke_Heindorf_Shaker_Hammer_Ngonga Ngomo_Hüllermeier_2021,
place={Cham}, title={Drift Detection in Text Data with Document Embeddings}, DOI={10.1007/978-3-030-91608-4_11},
booktitle={Intelligent Data Engineering and Automated Learning – IDEAL 2021},
publisher={Springer International Publishing}, author={Feldhans, Robert and Wilke,
Adrian and Heindorf, Stefan and Shaker, Mohammad Hossein and Hammer, Barbara and
Ngonga Ngomo, Axel-Cyrille and Hüllermeier, Eyke}, year={2021} }'
chicago: 'Feldhans, Robert, Adrian Wilke, Stefan Heindorf, Mohammad Hossein Shaker,
Barbara Hammer, Axel-Cyrille Ngonga Ngomo, and Eyke Hüllermeier. “Drift Detection
in Text Data with Document Embeddings.” In Intelligent Data Engineering and
Automated Learning – IDEAL 2021. Cham: Springer International Publishing,
2021. https://doi.org/10.1007/978-3-030-91608-4_11.'
ieee: 'R. Feldhans et al., “Drift Detection in Text Data with Document Embeddings,”
in Intelligent Data Engineering and Automated Learning – IDEAL 2021, Cham:
Springer International Publishing, 2021.'
mla: Feldhans, Robert, et al. “Drift Detection in Text Data with Document Embeddings.”
Intelligent Data Engineering and Automated Learning – IDEAL 2021, Springer
International Publishing, 2021, doi:10.1007/978-3-030-91608-4_11.
short: 'R. Feldhans, A. Wilke, S. Heindorf, M.H. Shaker, B. Hammer, A.-C. Ngonga
Ngomo, E. Hüllermeier, in: Intelligent Data Engineering and Automated Learning
– IDEAL 2021, Springer International Publishing, Cham, 2021.'
date_created: 2022-01-12T10:27:23Z
date_updated: 2022-10-15T19:54:20Z
department:
- _id: '574'
doi: 10.1007/978-3-030-91608-4_11
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://papers.dice-research.org/2021/IDEAL2021_DriftDetectionEmbeddings/Drift-Detection-in-Text-Data-with-Document-Embeddings-public.pdf
oa: '1'
place: Cham
publication: Intelligent Data Engineering and Automated Learning – IDEAL 2021
publication_identifier:
isbn:
- '9783030916077'
- '9783030916084'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer International Publishing
related_material:
link:
- relation: confirmation
url: https://link.springer.com/chapter/10.1007/978-3-030-91608-4_11
status: public
title: Drift Detection in Text Data with Document Embeddings
type: book_chapter
user_id: '11871'
year: '2021'
...
---
_id: '24456'
abstract:
- lang: eng
text: One objective of current research in explainable intelligent systems is to
implement social aspects in order to increase the relevance of explanations. In
this paper, we argue that a novel conceptual framework is needed to overcome shortcomings
of existing AI systems with little attention to processes of interaction and learning.
Drawing from research in interaction and development, we first outline the novel
conceptual framework that pushes the design of AI systems toward true interactivity
with an emphasis on the role of the partner and social relevance. We propose that
AI systems will be able to provide a meaningful and relevant explanation only
if the process of explaining is extended to active contribution of both partners
that brings about dynamics that is modulated by different levels of analysis.
Accordingly, our conceptual framework comprises monitoring and scaffolding as
key concepts and claims that the process of explaining is not only modulated by
the interaction between explainee and explainer but is embedded into a larger
social context in which conventionalized and routinized behaviors are established.
We discuss our conceptual framework in relation to the established objectives
of transparency and autonomy that are raised for the design of explainable AI
systems currently.
article_type: original
author:
- first_name: Katharina J.
full_name: Rohlfing, Katharina J.
id: '50352'
last_name: Rohlfing
- first_name: Philipp
full_name: Cimiano, Philipp
last_name: Cimiano
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Tobias
full_name: Matzner, Tobias
id: '65695'
last_name: Matzner
- first_name: Heike M.
full_name: Buhl, Heike M.
id: '27152'
last_name: Buhl
- first_name: Hendrik
full_name: Buschmeier, Hendrik
last_name: Buschmeier
- first_name: Elena
full_name: Esposito, Elena
last_name: Esposito
- first_name: Angela
full_name: Grimminger, Angela
id: '57578'
last_name: Grimminger
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Reinhold
full_name: Haeb-Umbach, Reinhold
id: '242'
last_name: Haeb-Umbach
- first_name: Ilona
full_name: Horwath, Ilona
id: '68836'
last_name: Horwath
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Friederike
full_name: Kern, Friederike
last_name: Kern
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Carsten
full_name: Schulte, Carsten
id: '60311'
last_name: Schulte
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
- first_name: Petra
full_name: Wagner, Petra
last_name: Wagner
- first_name: Britta
full_name: Wrede, Britta
last_name: Wrede
citation:
ama: 'Rohlfing KJ, Cimiano P, Scharlau I, et al. Explanation as a Social Practice:
Toward a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems. 2021;13(3):717-728. doi:10.1109/tcds.2020.3044366'
apa: 'Rohlfing, K. J., Cimiano, P., Scharlau, I., Matzner, T., Buhl, H. M., Buschmeier,
H., Esposito, E., Grimminger, A., Hammer, B., Haeb-Umbach, R., Horwath, I., Hüllermeier,
E., Kern, F., Kopp, S., Thommes, K., Ngonga Ngomo, A.-C., Schulte, C., Wachsmuth,
H., Wagner, P., & Wrede, B. (2021). Explanation as a Social Practice: Toward
a Conceptual Framework for the Social Design of AI Systems. IEEE Transactions
on Cognitive and Developmental Systems, 13(3), 717–728. https://doi.org/10.1109/tcds.2020.3044366'
bibtex: '@article{Rohlfing_Cimiano_Scharlau_Matzner_Buhl_Buschmeier_Esposito_Grimminger_Hammer_Haeb-Umbach_et
al._2021, title={Explanation as a Social Practice: Toward a Conceptual Framework
for the Social Design of AI Systems}, volume={13}, DOI={10.1109/tcds.2020.3044366},
number={3}, journal={IEEE Transactions on Cognitive and Developmental Systems},
author={Rohlfing, Katharina J. and Cimiano, Philipp and Scharlau, Ingrid and Matzner,
Tobias and Buhl, Heike M. and Buschmeier, Hendrik and Esposito, Elena and Grimminger,
Angela and Hammer, Barbara and Haeb-Umbach, Reinhold and et al.}, year={2021},
pages={717–728} }'
chicago: 'Rohlfing, Katharina J., Philipp Cimiano, Ingrid Scharlau, Tobias Matzner,
Heike M. Buhl, Hendrik Buschmeier, Elena Esposito, et al. “Explanation as a Social
Practice: Toward a Conceptual Framework for the Social Design of AI Systems.”
IEEE Transactions on Cognitive and Developmental Systems 13, no. 3 (2021):
717–28. https://doi.org/10.1109/tcds.2020.3044366.'
ieee: 'K. J. Rohlfing et al., “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems,” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, pp. 717–728, 2021,
doi: 10.1109/tcds.2020.3044366.'
mla: 'Rohlfing, Katharina J., et al. “Explanation as a Social Practice: Toward a
Conceptual Framework for the Social Design of AI Systems.” IEEE Transactions
on Cognitive and Developmental Systems, vol. 13, no. 3, 2021, pp. 717–28,
doi:10.1109/tcds.2020.3044366.'
short: K.J. Rohlfing, P. Cimiano, I. Scharlau, T. Matzner, H.M. Buhl, H. Buschmeier,
E. Esposito, A. Grimminger, B. Hammer, R. Haeb-Umbach, I. Horwath, E. Hüllermeier,
F. Kern, S. Kopp, K. Thommes, A.-C. Ngonga Ngomo, C. Schulte, H. Wachsmuth, P.
Wagner, B. Wrede, IEEE Transactions on Cognitive and Developmental Systems 13
(2021) 717–728.
date_created: 2021-09-14T20:52:57Z
date_updated: 2023-12-05T10:15:02Z
ddc:
- '300'
department:
- _id: '603'
- _id: '749'
- _id: '424'
- _id: '67'
- _id: '574'
- _id: '184'
- _id: '757'
- _id: '54'
- _id: '178'
doi: 10.1109/tcds.2020.3044366
file:
- access_level: open_access
content_type: application/pdf
creator: haebumb
date_created: 2023-11-20T16:33:51Z
date_updated: 2023-11-20T16:33:51Z
file_id: '49081'
file_name: 2020-12-01_explainability_final_version.pdf
file_size: 626217
relation: main_file
file_date_updated: 2023-11-20T16:33:51Z
has_accepted_license: '1'
intvolume: ' 13'
issue: '3'
keyword:
- Explainability
- process ofexplaining andunderstanding
- explainable artificial systems
language:
- iso: eng
oa: '1'
page: 717-728
project:
- _id: '109'
grant_number: '438445824'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
publication: IEEE Transactions on Cognitive and Developmental Systems
publication_identifier:
issn:
- 2379-8920
- 2379-8939
publication_status: published
quality_controlled: '1'
status: public
title: 'Explanation as a Social Practice: Toward a Conceptual Framework for the Social
Design of AI Systems'
type: journal_article
user_id: '42933'
volume: 13
year: '2021'
...
---
_id: '45616'
abstract:
- lang: eng
text: Aggregation metrics in reputation systems are important for overcoming information
overload. When using these metrics, technical aggregation functions such as the
arithmetic mean are implemented to measure the valence of product ratings. However,
it is unclear whether the implemented aggregation functions match the inherent
aggregation patterns of customers. In our experiment, we elicit customers' aggregation
heuristics and contrast these with reference functions. Our findings indicate
that, overall, the arithmetic mean performs best in comparison with other aggregation
functions. However, our analysis on an individual level reveals heterogeneous
aggregation patterns. Major clusters exhibit a binary bias (i.e., an over-weighting
of moderate ratings and under-weighting of extreme ratings) in combination with
the arithmetic mean. Minor clusters focus on 1-star ratings or negative (i.e.,
1-star and 2-star) ratings. Thereby, inherent aggregation patterns are neither
affected by variation of provided information nor by individual characteristics
such as experience, risk attitudes, or demographics.
author:
- first_name: Dirk
full_name: van Straaten, Dirk
id: '10311'
last_name: van Straaten
- first_name: Vitalik
full_name: Melnikov, Vitalik
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Behnud
full_name: Mir Djawadi, Behnud
id: '26032'
last_name: Mir Djawadi
orcid: 0000-0002-6271-5912
- first_name: René
full_name: Fahr, René
id: '111'
last_name: Fahr
citation:
ama: 'van Straaten D, Melnikov V, Hüllermeier E, Mir Djawadi B, Fahr R. Accounting
for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation
Processes. Vol 72.; 2021.'
apa: 'van Straaten, D., Melnikov, V., Hüllermeier, E., Mir Djawadi, B., & Fahr,
R. (2021). Accounting for Heuristics in Reputation Systems: An Interdisciplinary
Approach on Aggregation Processes (Vol. 72).'
bibtex: '@book{van Straaten_Melnikov_Hüllermeier_Mir Djawadi_Fahr_2021, series={Working
Papers Dissertations}, title={Accounting for Heuristics in Reputation Systems:
An Interdisciplinary Approach on Aggregation Processes}, volume={72}, author={van
Straaten, Dirk and Melnikov, Vitalik and Hüllermeier, Eyke and Mir Djawadi, Behnud
and Fahr, René}, year={2021}, collection={Working Papers Dissertations} }'
chicago: 'Straaten, Dirk van, Vitalik Melnikov, Eyke Hüllermeier, Behnud Mir Djawadi,
and René Fahr. Accounting for Heuristics in Reputation Systems: An Interdisciplinary
Approach on Aggregation Processes. Vol. 72. Working Papers Dissertations,
2021.'
ieee: 'D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, and R. Fahr,
Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach
on Aggregation Processes, vol. 72. 2021.'
mla: 'van Straaten, Dirk, et al. Accounting for Heuristics in Reputation Systems:
An Interdisciplinary Approach on Aggregation Processes. 2021.'
short: 'D. van Straaten, V. Melnikov, E. Hüllermeier, B. Mir Djawadi, R. Fahr, Accounting
for Heuristics in Reputation Systems: An Interdisciplinary Approach on Aggregation
Processes, 2021.'
date_created: 2023-06-15T08:23:33Z
date_updated: 2023-07-05T07:27:17Z
intvolume: ' 72'
language:
- iso: eng
project:
- _id: '8'
grant_number: '160364472'
name: 'SFB 901 - A4: SFB 901 - Empirische Analysen in Märkten für OTF Dienstleistungen
(Subproject A4)'
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '2'
name: 'SFB 901 - A: SFB 901 - Project Area A'
series_title: Working Papers Dissertations
status: public
title: 'Accounting for Heuristics in Reputation Systems: An Interdisciplinary Approach
on Aggregation Processes'
type: working_paper
user_id: '477'
volume: 72
year: '2021'
...
---
_id: '19603'
abstract:
- lang: eng
text: "Micro- and smart grids (MSG) play an important role both for integrating\r\nrenewable
energy sources in conventional electricity grids and for providing\r\npower supply
in remote areas. Modern MSGs are largely driven by power\r\nelectronic converters
due to their high efficiency and flexibility.\r\nNevertheless, controlling MSGs
is a challenging task due to highest\r\nrequirements on energy availability, safety
and voltage quality within a wide\r\nrange of different MSG topologies. This results
in a high demand for\r\ncomprehensive testing of new control concepts during their
development phase\r\nand comparisons with the state of the art in order to ensure
their feasibility.\r\nThis applies in particular to data-driven control approaches
from the field of\r\nreinforcement learning (RL), whose stability and operating
behavior can hardly\r\nbe evaluated a priori. Therefore, the OpenModelica Microgrid
Gym (OMG) package,\r\nan open-source software toolbox for the simulation and control
optimization of\r\nMSGs, is proposed. It is capable of modeling and simulating
arbitrary MSG\r\ntopologies and offers a Python-based interface for plug \\& play
controller\r\ntesting. In particular, the standardized OpenAI Gym interface allows
for easy\r\nRL-based controller integration. Besides the presentation of the OMG
toolbox,\r\napplication examples are highlighted including safe Bayesian optimization
for\r\nlow-level controller tuning."
author:
- first_name: Henrik
full_name: Bode, Henrik
last_name: Bode
- first_name: Stefan Helmut
full_name: Heid, Stefan Helmut
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Daniel
full_name: Weber, Daniel
last_name: Weber
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Oliver
full_name: Wallscheid, Oliver
last_name: Wallscheid
citation:
ama: Bode H, Heid SH, Weber D, Hüllermeier E, Wallscheid O. Towards a Scalable and
Flexible Simulation and Testing Environment Toolbox for Intelligent Microgrid
Control. arXiv:200504869. 2020.
apa: Bode, H., Heid, S. H., Weber, D., Hüllermeier, E., & Wallscheid, O. (2020).
Towards a Scalable and Flexible Simulation and Testing Environment Toolbox for
Intelligent Microgrid Control. ArXiv:2005.04869.
bibtex: '@article{Bode_Heid_Weber_Hüllermeier_Wallscheid_2020, title={Towards a
Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent
Microgrid Control}, journal={arXiv:2005.04869}, author={Bode, Henrik and Heid,
Stefan Helmut and Weber, Daniel and Hüllermeier, Eyke and Wallscheid, Oliver},
year={2020} }'
chicago: Bode, Henrik, Stefan Helmut Heid, Daniel Weber, Eyke Hüllermeier, and Oliver
Wallscheid. “Towards a Scalable and Flexible Simulation and Testing Environment
Toolbox for Intelligent Microgrid Control.” ArXiv:2005.04869, 2020.
ieee: H. Bode, S. H. Heid, D. Weber, E. Hüllermeier, and O. Wallscheid, “Towards
a Scalable and Flexible Simulation and Testing Environment Toolbox for Intelligent
Microgrid Control,” arXiv:2005.04869. 2020.
mla: Bode, Henrik, et al. “Towards a Scalable and Flexible Simulation and Testing
Environment Toolbox for Intelligent Microgrid Control.” ArXiv:2005.04869,
2020.
short: H. Bode, S.H. Heid, D. Weber, E. Hüllermeier, O. Wallscheid, ArXiv:2005.04869
(2020).
date_created: 2020-09-21T10:01:36Z
date_updated: 2022-01-06T06:54:07Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/2005.04869.pdf
oa: '1'
publication: arXiv:2005.04869
status: public
title: Towards a Scalable and Flexible Simulation and Testing Environment Toolbox
for Intelligent Microgrid Control
type: preprint
user_id: '39640'
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: '20211'
abstract:
- lang: eng
text: "In many real-world applications, the relative depth of objects in an image
is\r\ncrucial for scene understanding, e.g., to calculate occlusions in augmented\r\nreality
scenes. Predicting depth in monocular images has recently been tackled\r\nusing
machine learning methods, mainly by treating the problem as a regression\r\ntask.
Yet, being interested in an order relation in the first place,\r\nranking methods
suggest themselves as a natural alternative to regression, and\r\nindeed, ranking
approaches leveraging pairwise comparisons as training\r\ninformation (\"object
A is closer to the camera than B\") have shown promising\r\nperformance on this
problem. In this paper, we elaborate on the use of\r\nso-called \\emph{listwise}
ranking as a generalization of the pairwise approach.\r\nListwise ranking goes
beyond pairwise comparisons between objects and considers\r\nrankings of arbitrary
length as training information. Our approach is based on\r\nthe Plackett-Luce
model, a probability distribution on rankings, which we\r\ncombine with a state-of-the-art
neural network architecture and a sampling\r\nstrategy to reduce training complexity.
An empirical evaluation on benchmark\r\ndata in a \"zero-shot\" setting demonstrates
the effectiveness of our proposal\r\ncompared to existing ranking and regression
methods."
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Lienen J, Hüllermeier E. Monocular Depth Estimation via Listwise Ranking using
the Plackett-Luce model. arXiv:201013118. 2020.
apa: Lienen, J., & Hüllermeier, E. (2020). Monocular Depth Estimation via Listwise
Ranking using the Plackett-Luce model. ArXiv:2010.13118.
bibtex: '@article{Lienen_Hüllermeier_2020, title={Monocular Depth Estimation via
Listwise Ranking using the Plackett-Luce model}, journal={arXiv:2010.13118},
author={Lienen, Julian and Hüllermeier, Eyke}, year={2020} }'
chicago: Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise
Ranking Using the Plackett-Luce Model.” ArXiv:2010.13118, 2020.
ieee: J. Lienen and E. Hüllermeier, “Monocular Depth Estimation via Listwise Ranking
using the Plackett-Luce model,” arXiv:2010.13118. 2020.
mla: Lienen, Julian, and Eyke Hüllermeier. “Monocular Depth Estimation via Listwise
Ranking Using the Plackett-Luce Model.” ArXiv:2010.13118, 2020.
short: J. Lienen, E. Hüllermeier, ArXiv:2010.13118 (2020).
date_created: 2020-10-27T07:48:40Z
date_updated: 2022-01-06T06:54:23Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2010.13118
oa: '1'
publication: arXiv:2010.13118
status: public
title: Monocular Depth Estimation via Listwise Ranking using the Plackett-Luce model
type: preprint
user_id: '44040'
year: '2020'
...
---
_id: '24146'
author:
- first_name: Stefan Helmut
full_name: Heid, Stefan Helmut
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Heid SH, Ramaswamy A, Hüllermeier E. Constrained Multi-Agent Optimization
with Unbounded Information Delay. In: Proceedings-30. Workshop Computational
Intelligence: Berlin, 26.-27. November 2020. Vol 26. ; 2020:247.'
apa: 'Heid, S. H., Ramaswamy, A., & Hüllermeier, E. (2020). Constrained Multi-Agent
Optimization with Unbounded Information Delay. Proceedings-30. Workshop Computational
Intelligence: Berlin, 26.-27. November 2020, 26, 247.'
bibtex: '@inproceedings{Heid_Ramaswamy_Hüllermeier_2020, title={Constrained Multi-Agent
Optimization with Unbounded Information Delay}, volume={26}, booktitle={Proceedings-30.
Workshop Computational Intelligence: Berlin, 26.-27. November 2020}, author={Heid,
Stefan Helmut and Ramaswamy, Arunselvan and Hüllermeier, Eyke}, year={2020}, pages={247}
}'
chicago: 'Heid, Stefan Helmut, Arunselvan Ramaswamy, and Eyke Hüllermeier. “Constrained
Multi-Agent Optimization with Unbounded Information Delay.” In Proceedings-30.
Workshop Computational Intelligence: Berlin, 26.-27. November 2020, 26:247,
2020.'
ieee: 'S. H. Heid, A. Ramaswamy, and E. Hüllermeier, “Constrained Multi-Agent Optimization
with Unbounded Information Delay,” in Proceedings-30. Workshop Computational
Intelligence: Berlin, 26.-27. November 2020, 2020, vol. 26, p. 247.'
mla: 'Heid, Stefan Helmut, et al. “Constrained Multi-Agent Optimization with Unbounded
Information Delay.” Proceedings-30. Workshop Computational Intelligence: Berlin,
26.-27. November 2020, vol. 26, 2020, p. 247.'
short: 'S.H. Heid, A. Ramaswamy, E. Hüllermeier, in: Proceedings-30. Workshop Computational
Intelligence: Berlin, 26.-27. November 2020, 2020, p. 247.'
date_created: 2021-09-10T09:59:16Z
date_updated: 2022-01-06T06:56:08Z
intvolume: ' 26'
language:
- iso: eng
page: '247'
publication: 'Proceedings-30. Workshop Computational Intelligence: Berlin, 26.-27.
November 2020'
status: public
title: Constrained Multi-Agent Optimization with Unbounded Information Delay
type: conference
user_id: '66937'
volume: 26
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'
...