---
_id: '18018'
abstract:
- lang: eng
text: |-
A common statistical task lies in showing asymptotic normality of certain
statistics. In many of these situations, classical textbook results on weak
convergence theory suffice for the problem at hand. However, there are quite
some scenarios where stronger results are needed in order to establish an
asymptotic normal approximation uniformly over a family of probability
measures. In this note we collect some results in this direction. We restrict
ourselves to weak convergence in $\mathbb R^d$ with continuous limit measures.
author:
- first_name: Viktor
full_name: Bengs, Viktor
last_name: Bengs
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Holzmann H. Uniform approximation in classical weak convergence theory.
arXiv:190309864. 2019.
apa: Bengs, V., & Holzmann, H. (2019). Uniform approximation in classical weak
convergence theory. ArXiv:1903.09864.
bibtex: '@article{Bengs_Holzmann_2019, title={Uniform approximation in classical
weak convergence theory}, journal={arXiv:1903.09864}, author={Bengs, Viktor and
Holzmann, Hajo}, year={2019} }'
chicago: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak
Convergence Theory.” ArXiv:1903.09864, 2019.
ieee: V. Bengs and H. Holzmann, “Uniform approximation in classical weak convergence
theory,” arXiv:1903.09864. 2019.
mla: Bengs, Viktor, and Hajo Holzmann. “Uniform Approximation in Classical Weak
Convergence Theory.” ArXiv:1903.09864, 2019.
short: V. Bengs, H. Holzmann, ArXiv:1903.09864 (2019).
date_created: 2020-08-17T12:10:55Z
date_updated: 2022-01-06T06:53:25Z
department:
- _id: '34'
- _id: '7'
- _id: '355'
publication: arXiv:1903.09864
status: public
title: Uniform approximation in classical weak convergence theory
type: preprint
user_id: '76599'
year: '2019'
...
---
_id: '8868'
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Alexander
full_name: Hetzer, Alexander
id: '38209'
last_name: Hetzer
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E, Hetzer A. Towards Automated Machine Learning
for Multi-Label Classification. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Hüllermeier, E., & Hetzer, A. (2019). Towards Automated
Machine Learning for Multi-Label Classification. Presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_Hetzer_2019, title={Towards Automated
Machine Learning for Multi-Label Classification}, author={Wever, Marcel Dominik
and Mohr, Felix and Hüllermeier, Eyke and Hetzer, Alexander}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Eyke Hüllermeier, and Alexander Hetzer.
“Towards Automated Machine Learning for Multi-Label Classification,” 2019.
ieee: M. D. Wever, F. Mohr, E. Hüllermeier, and A. Hetzer, “Towards Automated Machine
Learning for Multi-Label Classification,” presented at the European Conference
on Data Analytics (ECDA), Bayreuth, Germany, 2019.
mla: Wever, Marcel Dominik, et al. Towards Automated Machine Learning for Multi-Label
Classification. 2019.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, A. Hetzer, in: 2019.'
conference:
end_date: 2019-03-20
location: Bayreuth, Germany
name: European Conference on Data Analytics (ECDA)
start_date: 2019-03-18
date_created: 2019-04-10T07:17:55Z
date_updated: 2022-01-06T07:04:04Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2019-04-10T07:17:17Z
date_updated: 2019-04-10T07:17:17Z
file_id: '8870'
file_name: Towards_Automated_Machine_Learning_for_Multi_Label_Classification.pdf
file_size: '74484'
relation: main_file
success: 1
file_date_updated: 2019-04-10T07:17:17Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Towards Automated Machine Learning for Multi-Label Classification
type: conference_abstract
user_id: '49109'
year: '2019'
...
---
_id: '10578'
author:
- first_name: V. K.
full_name: Tagne, V. K.
last_name: Tagne
- first_name: S.
full_name: Fotso, S.
last_name: Fotso
- first_name: 'L. A. '
full_name: 'Fono, L. A. '
last_name: Fono
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Tagne VK, Fotso S, Fono LA, Hüllermeier E. Choice Functions Generated by Mallows
and Plackett–Luce Relations. New Mathematics and Natural Computation. 2019;15(2):191-213.
apa: Tagne, V. K., Fotso, S., Fono, L. A., & Hüllermeier, E. (2019). Choice
Functions Generated by Mallows and Plackett–Luce Relations. New Mathematics
and Natural Computation, 15(2), 191–213.
bibtex: '@article{Tagne_Fotso_Fono_Hüllermeier_2019, title={Choice Functions Generated
by Mallows and Plackett–Luce Relations}, volume={15}, number={2}, journal={New
Mathematics and Natural Computation}, author={Tagne, V. K. and Fotso, S. and Fono,
L. A. and Hüllermeier, Eyke}, year={2019}, pages={191–213} }'
chicago: 'Tagne, V. K., S. Fotso, L. A. Fono, and Eyke Hüllermeier. “Choice Functions
Generated by Mallows and Plackett–Luce Relations.” New Mathematics and Natural
Computation 15, no. 2 (2019): 191–213.'
ieee: V. K. Tagne, S. Fotso, L. A. Fono, and E. Hüllermeier, “Choice Functions Generated
by Mallows and Plackett–Luce Relations,” New Mathematics and Natural Computation,
vol. 15, no. 2, pp. 191–213, 2019.
mla: Tagne, V. K., et al. “Choice Functions Generated by Mallows and Plackett–Luce
Relations.” New Mathematics and Natural Computation, vol. 15, no. 2, 2019,
pp. 191–213.
short: V.K. Tagne, S. Fotso, L.A. Fono, E. Hüllermeier, New Mathematics and Natural
Computation 15 (2019) 191–213.
date_created: 2019-07-08T15:34:03Z
date_updated: 2022-01-06T06:50:45Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
intvolume: ' 15'
issue: '2'
language:
- iso: eng
page: 191-213
publication: New Mathematics and Natural Computation
status: public
title: Choice Functions Generated by Mallows and Plackett–Luce Relations
type: journal_article
user_id: '315'
volume: 15
year: '2019'
...
---
_id: '15001'
author:
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: Christian
full_name: Borgelt, Christian
last_name: Borgelt
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Rudolf
full_name: Kruse, Rudolf
last_name: Kruse
citation:
ama: 'Couso I, Borgelt C, Hüllermeier E, Kruse R. Fuzzy Sets in Data Analysis: From
Statistical Foundations to Machine Learning. IEEE Computational Intelligence
Magazine. 2019:31-44. doi:10.1109/mci.2018.2881642'
apa: 'Couso, I., Borgelt, C., Hüllermeier, E., & Kruse, R. (2019). Fuzzy Sets
in Data Analysis: From Statistical Foundations to Machine Learning. IEEE Computational
Intelligence Magazine, 31–44. https://doi.org/10.1109/mci.2018.2881642'
bibtex: '@article{Couso_Borgelt_Hüllermeier_Kruse_2019, title={Fuzzy Sets in Data
Analysis: From Statistical Foundations to Machine Learning}, DOI={10.1109/mci.2018.2881642},
journal={IEEE Computational Intelligence Magazine}, author={Couso, Ines and Borgelt,
Christian and Hüllermeier, Eyke and Kruse, Rudolf}, year={2019}, pages={31–44}
}'
chicago: 'Couso, Ines, Christian Borgelt, Eyke Hüllermeier, and Rudolf Kruse. “Fuzzy
Sets in Data Analysis: From Statistical Foundations to Machine Learning.” IEEE
Computational Intelligence Magazine, 2019, 31–44. https://doi.org/10.1109/mci.2018.2881642.'
ieee: 'I. Couso, C. Borgelt, E. Hüllermeier, and R. Kruse, “Fuzzy Sets in Data Analysis:
From Statistical Foundations to Machine Learning,” IEEE Computational Intelligence
Magazine, pp. 31–44, 2019.'
mla: 'Couso, Ines, et al. “Fuzzy Sets in Data Analysis: From Statistical Foundations
to Machine Learning.” IEEE Computational Intelligence Magazine, 2019, pp.
31–44, doi:10.1109/mci.2018.2881642.'
short: I. Couso, C. Borgelt, E. Hüllermeier, R. Kruse, IEEE Computational Intelligence
Magazine (2019) 31–44.
date_created: 2019-11-15T10:11:37Z
date_updated: 2022-01-06T06:52:13Z
department:
- _id: '34'
- _id: '355'
doi: 10.1109/mci.2018.2881642
language:
- iso: eng
page: 31-44
publication: IEEE Computational Intelligence Magazine
publication_identifier:
issn:
- 1556-603X
- 1556-6048
publication_status: published
status: public
title: 'Fuzzy Sets in Data Analysis: From Statistical Foundations to Machine Learning'
type: journal_article
user_id: '315'
year: '2019'
...
---
_id: '15002'
abstract:
- lang: eng
text: Many problem settings in machine learning are concerned with the simultaneous
prediction of multiple target variables of diverse type. Amongst others, such
problem settings arise in multivariate regression, multi-label classification,
multi-task learning, dyadic prediction, zero-shot learning, network inference,
and matrix completion. These subfields of machine learning are typically studied
in isolation, without highlighting or exploring important relationships. In this
paper, we present a unifying view on what we call multi-target prediction (MTP)
problems and methods. First, we formally discuss commonalities and differences
between existing MTP problems. To this end, we introduce a general framework that
covers the above subfields as special cases. As a second contribution, we provide
a structured overview of MTP methods. This is accomplished by identifying a number
of key properties, which distinguish such methods and determine their suitability
for different types of problems. Finally, we also discuss a few challenges for
future research.
author:
- first_name: Willem
full_name: Waegeman, Willem
last_name: Waegeman
- first_name: Krzysztof
full_name: Dembczynski, Krzysztof
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Waegeman W, Dembczynski K, Hüllermeier E. Multi-target prediction: a unifying
view on problems and methods. Data Mining and Knowledge Discovery. 2019;33(2):293-324.
doi:10.1007/s10618-018-0595-5'
apa: 'Waegeman, W., Dembczynski, K., & Hüllermeier, E. (2019). Multi-target
prediction: a unifying view on problems and methods. Data Mining and Knowledge
Discovery, 33(2), 293–324. https://doi.org/10.1007/s10618-018-0595-5'
bibtex: '@article{Waegeman_Dembczynski_Hüllermeier_2019, title={Multi-target prediction:
a unifying view on problems and methods}, volume={33}, DOI={10.1007/s10618-018-0595-5},
number={2}, journal={Data Mining and Knowledge Discovery}, author={Waegeman, Willem
and Dembczynski, Krzysztof and Hüllermeier, Eyke}, year={2019}, pages={293–324}
}'
chicago: 'Waegeman, Willem, Krzysztof Dembczynski, and Eyke Hüllermeier. “Multi-Target
Prediction: A Unifying View on Problems and Methods.” Data Mining and Knowledge
Discovery 33, no. 2 (2019): 293–324. https://doi.org/10.1007/s10618-018-0595-5.'
ieee: 'W. Waegeman, K. Dembczynski, and E. Hüllermeier, “Multi-target prediction:
a unifying view on problems and methods,” Data Mining and Knowledge Discovery,
vol. 33, no. 2, pp. 293–324, 2019.'
mla: 'Waegeman, Willem, et al. “Multi-Target Prediction: A Unifying View on Problems
and Methods.” Data Mining and Knowledge Discovery, vol. 33, no. 2, 2019,
pp. 293–324, doi:10.1007/s10618-018-0595-5.'
short: W. Waegeman, K. Dembczynski, E. Hüllermeier, Data Mining and Knowledge Discovery
33 (2019) 293–324.
date_created: 2019-11-15T10:16:34Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
doi: 10.1007/s10618-018-0595-5
file:
- access_level: open_access
content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:43:39Z
date_updated: 2020-02-28T12:45:26Z
file_id: '16155'
file_name: multi-target-prediction.pdf
file_size: 837808
relation: main_file
file_date_updated: 2020-02-28T12:45:26Z
has_accepted_license: '1'
intvolume: ' 33'
issue: '2'
language:
- iso: eng
oa: '1'
page: 293-324
publication: Data Mining and Knowledge Discovery
publication_identifier:
issn:
- 1573-756X
status: public
title: 'Multi-target prediction: a unifying view on problems and methods'
type: journal_article
user_id: '315'
volume: 33
year: '2019'
...
---
_id: '15003'
author:
- first_name: Thomas
full_name: Mortier, Thomas
last_name: Mortier
- first_name: Marek
full_name: Wydmuch, Marek
last_name: Wydmuch
- first_name: Krzysztof
full_name: Dembczynski, Krzysztof
last_name: Dembczynski
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Willem
full_name: Waegeman, Willem
last_name: Waegeman
citation:
ama: 'Mortier T, Wydmuch M, Dembczynski K, Hüllermeier E, Waegeman W. Set-Valued
Prediction in Multi-Class Classification. In: Proceedings of the 31st Benelux
Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch
Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8,
2019. ; 2019.'
apa: Mortier, T., Wydmuch, M., Dembczynski, K., Hüllermeier, E., & Waegeman,
W. (2019). Set-Valued Prediction in Multi-Class Classification. In Proceedings
of the 31st Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the
28th Belgian Dutch Conference on Machine Learning (Benelearn 2019), Brussels,
Belgium, November 6-8, 2019.
bibtex: '@inproceedings{Mortier_Wydmuch_Dembczynski_Hüllermeier_Waegeman_2019, title={Set-Valued
Prediction in Multi-Class Classification}, booktitle={Proceedings of the 31st
Benelux Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian
Dutch Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November
6-8, 2019}, author={Mortier, Thomas and Wydmuch, Marek and Dembczynski, Krzysztof
and Hüllermeier, Eyke and Waegeman, Willem}, year={2019} }'
chicago: Mortier, Thomas, Marek Wydmuch, Krzysztof Dembczynski, Eyke Hüllermeier,
and Willem Waegeman. “Set-Valued Prediction in Multi-Class Classification.” In
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.
ieee: T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, and W. Waegeman, “Set-Valued
Prediction in Multi-Class Classification,” in Proceedings of the 31st Benelux
Conference on Artificial Intelligence {(BNAIC} 2019) and the 28th Belgian Dutch
Conference on Machine Learning (Benelearn 2019), Brussels, Belgium, November 6-8,
2019, 2019.
mla: Mortier, Thomas, et al. “Set-Valued Prediction in Multi-Class Classification.”
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.
short: 'T. Mortier, M. Wydmuch, K. Dembczynski, E. Hüllermeier, W. Waegeman, in:
Proceedings of the 31st Benelux Conference on Artificial Intelligence {(BNAIC}
2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn 2019),
Brussels, Belgium, November 6-8, 2019, 2019.'
date_created: 2019-11-15T10:20:55Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
language:
- iso: eng
publication: Proceedings of the 31st Benelux Conference on Artificial Intelligence
{(BNAIC} 2019) and the 28th Belgian Dutch Conference on Machine Learning (Benelearn
2019), Brussels, Belgium, November 6-8, 2019
status: public
title: Set-Valued Prediction in Multi-Class Classification
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15004'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
id: '59547'
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Feature Selection for Analogy-Based Learning
to Rank. In: Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_22'
apa: Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Feature Selection for Analogy-Based
Learning to Rank. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_22
bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Feature
Selection for Analogy-Based Learning to Rank}, DOI={10.1007/978-3-030-33778-0_22},
booktitle={Discovery Science}, author={Ahmadi Fahandar, Mohsen and Hüllermeier,
Eyke}, year={2019} }'
chicago: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based
Learning to Rank.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_22.
ieee: M. Ahmadi Fahandar and E. Hüllermeier, “Feature Selection for Analogy-Based
Learning to Rank,” in Discovery Science, Cham, 2019.
mla: Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Feature Selection for Analogy-Based
Learning to Rank.” Discovery Science, 2019, doi:10.1007/978-3-030-33778-0_22.
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: Discovery Science, Cham, 2019.'
date_created: 2019-11-15T10:24:45Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-33778-0_22
language:
- iso: eng
place: Cham
publication: Discovery Science
publication_identifier:
isbn:
- '9783030337773'
- '9783030337780'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Feature Selection for Analogy-Based Learning to Rank
type: book_chapter
user_id: '315'
year: '2019'
...
---
_id: '15005'
author:
- first_name: Mohsen
full_name: Ahmadi Fahandar, Mohsen
id: '59547'
last_name: Ahmadi Fahandar
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Ahmadi Fahandar M, Hüllermeier E. Analogy-Based Preference Learning with Kernels.
In: KI 2019: Advances in Artificial Intelligence. Cham; 2019. doi:10.1007/978-3-030-30179-8_3'
apa: 'Ahmadi Fahandar, M., & Hüllermeier, E. (2019). Analogy-Based Preference
Learning with Kernels. In KI 2019: Advances in Artificial Intelligence.
Cham. https://doi.org/10.1007/978-3-030-30179-8_3'
bibtex: '@inbook{Ahmadi Fahandar_Hüllermeier_2019, place={Cham}, title={Analogy-Based
Preference Learning with Kernels}, DOI={10.1007/978-3-030-30179-8_3},
booktitle={KI 2019: Advances in Artificial Intelligence}, author={Ahmadi Fahandar,
Mohsen and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference
Learning with Kernels.” In KI 2019: Advances in Artificial Intelligence.
Cham, 2019. https://doi.org/10.1007/978-3-030-30179-8_3.'
ieee: 'M. Ahmadi Fahandar and E. Hüllermeier, “Analogy-Based Preference Learning
with Kernels,” in KI 2019: Advances in Artificial Intelligence, Cham, 2019.'
mla: 'Ahmadi Fahandar, Mohsen, and Eyke Hüllermeier. “Analogy-Based Preference Learning
with Kernels.” KI 2019: Advances in Artificial Intelligence, 2019, doi:10.1007/978-3-030-30179-8_3.'
short: 'M. Ahmadi Fahandar, E. Hüllermeier, in: KI 2019: Advances in Artificial
Intelligence, Cham, 2019.'
date_created: 2019-11-15T10:30:10Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-30179-8_3
language:
- iso: eng
place: Cham
publication: 'KI 2019: Advances in Artificial Intelligence'
publication_identifier:
isbn:
- '9783030301781'
- '9783030301798'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Analogy-Based Preference Learning with Kernels
type: book_chapter
user_id: '315'
year: '2019'
...
---
_id: '15006'
author:
- first_name: Vu-Linh
full_name: Nguyen, Vu-Linh
last_name: Nguyen
- first_name: Sébastien
full_name: Destercke, Sébastien
last_name: Destercke
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Nguyen V-L, Destercke S, Hüllermeier E. Epistemic Uncertainty Sampling. In:
Discovery Science. Cham; 2019. doi:10.1007/978-3-030-33778-0_7'
apa: Nguyen, V.-L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty
Sampling. In Discovery Science. Cham. https://doi.org/10.1007/978-3-030-33778-0_7
bibtex: '@inbook{Nguyen_Destercke_Hüllermeier_2019, place={Cham}, title={Epistemic
Uncertainty Sampling}, DOI={10.1007/978-3-030-33778-0_7},
booktitle={Discovery Science}, author={Nguyen, Vu-Linh and Destercke, Sébastien
and Hüllermeier, Eyke}, year={2019} }'
chicago: Nguyen, Vu-Linh, Sébastien Destercke, and Eyke Hüllermeier. “Epistemic
Uncertainty Sampling.” In Discovery Science. Cham, 2019. https://doi.org/10.1007/978-3-030-33778-0_7.
ieee: V.-L. Nguyen, S. Destercke, and E. Hüllermeier, “Epistemic Uncertainty Sampling,”
in Discovery Science, Cham, 2019.
mla: Nguyen, Vu-Linh, et al. “Epistemic Uncertainty Sampling.” Discovery Science,
2019, doi:10.1007/978-3-030-33778-0_7.
short: 'V.-L. Nguyen, S. Destercke, E. Hüllermeier, in: Discovery Science, Cham,
2019.'
date_created: 2019-11-15T10:35:08Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1007/978-3-030-33778-0_7
language:
- iso: eng
place: Cham
publication: Discovery Science
publication_identifier:
isbn:
- '9783030337773'
- '9783030337780'
issn:
- 0302-9743
- 1611-3349
publication_status: published
status: public
title: Epistemic Uncertainty Sampling
type: book_chapter
user_id: '49109'
year: '2019'
...
---
_id: '15007'
author:
- first_name: Vitaly
full_name: Melnikov, Vitaly
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA. In: Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101). ; 2019. doi:10.1016/j.jmva.2019.02.017'
apa: 'Melnikov, V., & Hüllermeier, E. (2019). Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA. In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101).
https://doi.org/10.1016/j.jmva.2019.02.017'
bibtex: '@inproceedings{Melnikov_Hüllermeier_2019, title={Learning to Aggregate:
Tackling the Aggregation/Disaggregation Problem for OWA}, DOI={10.1016/j.jmva.2019.02.017},
booktitle={Proceedings ACML, Asian Conference on Machine Learning (Proceedings
of Machine Learning Research, 101)}, author={Melnikov, Vitaly and Hüllermeier,
Eyke}, year={2019} }'
chicago: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling
the Aggregation/Disaggregation Problem for OWA.” In Proceedings ACML, Asian
Conference on Machine Learning (Proceedings of Machine Learning Research, 101),
2019. https://doi.org/10.1016/j.jmva.2019.02.017.'
ieee: 'V. Melnikov and E. Hüllermeier, “Learning to Aggregate: Tackling the Aggregation/Disaggregation
Problem for OWA,” in Proceedings ACML, Asian Conference on Machine Learning
(Proceedings of Machine Learning Research, 101), 2019.'
mla: 'Melnikov, Vitaly, and Eyke Hüllermeier. “Learning to Aggregate: Tackling the
Aggregation/Disaggregation Problem for OWA.” Proceedings ACML, Asian Conference
on Machine Learning (Proceedings of Machine Learning Research, 101), 2019,
doi:10.1016/j.jmva.2019.02.017.'
short: 'V. Melnikov, E. Hüllermeier, in: Proceedings ACML, Asian Conference on Machine
Learning (Proceedings of Machine Learning Research, 101), 2019.'
date_created: 2019-11-15T10:43:26Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '000'
department:
- _id: '34'
- _id: '355'
- _id: '7'
doi: 10.1016/j.jmva.2019.02.017
file:
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content_type: application/pdf
creator: lettmann
date_created: 2020-02-28T12:47:07Z
date_updated: 2020-02-28T12:47:07Z
file_id: '16156'
file_name: learning-to-aggregate-owa.pdf
file_size: 2331320
relation: main_file
file_date_updated: 2020-02-28T12:47:07Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Proceedings ACML, Asian Conference on Machine Learning (Proceedings of
Machine Learning Research, 101)
publication_status: published
status: public
title: 'Learning to Aggregate: Tackling the Aggregation/Disaggregation Problem for
OWA'
type: conference
user_id: '477'
year: '2019'
...
---
_id: '15009'
author:
- first_name: Nico
full_name: Epple, Nico
last_name: Epple
- first_name: Simone
full_name: Dari, Simone
last_name: Dari
- first_name: Ludwig
full_name: Drees, Ludwig
last_name: Drees
- first_name: Valentin
full_name: Protschky, Valentin
last_name: Protschky
- first_name: Andreas
full_name: Riener, Andreas
last_name: Riener
citation:
ama: 'Epple N, Dari S, Drees L, Protschky V, Riener A. Influence of Cruise Control
on Driver Guidance - a Comparison between System Generations and Countries. In:
2019 IEEE Intelligent Vehicles Symposium (IV). ; 2019. doi:10.1109/ivs.2019.8814100'
apa: Epple, N., Dari, S., Drees, L., Protschky, V., & Riener, A. (2019). Influence
of Cruise Control on Driver Guidance - a Comparison between System Generations
and Countries. In 2019 IEEE Intelligent Vehicles Symposium (IV). https://doi.org/10.1109/ivs.2019.8814100
bibtex: '@inproceedings{Epple_Dari_Drees_Protschky_Riener_2019, title={Influence
of Cruise Control on Driver Guidance - a Comparison between System Generations
and Countries}, DOI={10.1109/ivs.2019.8814100},
booktitle={2019 IEEE Intelligent Vehicles Symposium (IV)}, author={Epple, Nico
and Dari, Simone and Drees, Ludwig and Protschky, Valentin and Riener, Andreas},
year={2019} }'
chicago: Epple, Nico, Simone Dari, Ludwig Drees, Valentin Protschky, and Andreas
Riener. “Influence of Cruise Control on Driver Guidance - a Comparison between
System Generations and Countries.” In 2019 IEEE Intelligent Vehicles Symposium
(IV), 2019. https://doi.org/10.1109/ivs.2019.8814100.
ieee: N. Epple, S. Dari, L. Drees, V. Protschky, and A. Riener, “Influence of Cruise
Control on Driver Guidance - a Comparison between System Generations and Countries,”
in 2019 IEEE Intelligent Vehicles Symposium (IV), 2019.
mla: Epple, Nico, et al. “Influence of Cruise Control on Driver Guidance - a Comparison
between System Generations and Countries.” 2019 IEEE Intelligent Vehicles Symposium
(IV), 2019, doi:10.1109/ivs.2019.8814100.
short: 'N. Epple, S. Dari, L. Drees, V. Protschky, A. Riener, in: 2019 IEEE Intelligent
Vehicles Symposium (IV), 2019.'
date_created: 2019-11-15T10:54:04Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
doi: 10.1109/ivs.2019.8814100
language:
- iso: eng
publication: 2019 IEEE Intelligent Vehicles Symposium (IV)
publication_identifier:
isbn:
- '9781728105604'
publication_status: published
status: public
title: Influence of Cruise Control on Driver Guidance - a Comparison between System
Generations and Countries
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15011'
author:
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Tornede A, Wever MD, Hüllermeier E. Algorithm Selection as Recommendation:
From Collaborative Filtering to Dyad Ranking. In: Hoffmann F, Hüllermeier E, Mikut
R, eds. Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28.
- 29. November 2019. KIT Scientific Publishing, Karlsruhe; 2019:135-146.'
apa: 'Tornede, A., Wever, M. D., & Hüllermeier, E. (2019). Algorithm Selection
as Recommendation: From Collaborative Filtering to Dyad Ranking. In F. Hoffmann,
E. Hüllermeier, & R. Mikut (Eds.), Proceedings - 29. Workshop Computational
Intelligence, Dortmund, 28. - 29. November 2019 (pp. 135–146). Dortmund: KIT
Scientific Publishing, Karlsruhe.'
bibtex: '@inproceedings{Tornede_Wever_Hüllermeier_2019, title={Algorithm Selection
as Recommendation: From Collaborative Filtering to Dyad Ranking}, booktitle={Proceedings
- 29. Workshop Computational Intelligence, Dortmund, 28. - 29. November 2019},
publisher={KIT Scientific Publishing, Karlsruhe}, author={Tornede, Alexander and
Wever, Marcel Dominik and Hüllermeier, Eyke}, editor={Hoffmann, Frank and Hüllermeier,
Eyke and Mikut, RalfEditors}, year={2019}, pages={135–146} }'
chicago: 'Tornede, Alexander, Marcel Dominik Wever, and Eyke Hüllermeier. “Algorithm
Selection as Recommendation: From Collaborative Filtering to Dyad Ranking.” In
Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28. - 29.
November 2019, edited by Frank Hoffmann, Eyke Hüllermeier, and Ralf Mikut,
135–46. KIT Scientific Publishing, Karlsruhe, 2019.'
ieee: 'A. Tornede, M. D. Wever, and E. Hüllermeier, “Algorithm Selection as Recommendation:
From Collaborative Filtering to Dyad Ranking,” in Proceedings - 29. Workshop
Computational Intelligence, Dortmund, 28. - 29. November 2019, Dortmund, 2019,
pp. 135–146.'
mla: 'Tornede, Alexander, et al. “Algorithm Selection as Recommendation: From Collaborative
Filtering to Dyad Ranking.” Proceedings - 29. Workshop Computational Intelligence,
Dortmund, 28. - 29. November 2019, edited by Frank Hoffmann et al., KIT Scientific
Publishing, Karlsruhe, 2019, pp. 135–46.'
short: 'A. Tornede, M.D. Wever, E. Hüllermeier, in: F. Hoffmann, E. Hüllermeier,
R. Mikut (Eds.), Proceedings - 29. Workshop Computational Intelligence, Dortmund,
28. - 29. November 2019, KIT Scientific Publishing, Karlsruhe, 2019, pp. 135–146.'
conference:
end_date: 2019-11-29
location: Dortmund
name: 29. Workshop Computational Intelligence
start_date: 2019-11-28
date_created: 2019-11-15T13:29:25Z
date_updated: 2022-01-06T06:52:14Z
ddc:
- '006'
department:
- _id: '355'
editor:
- first_name: Frank
full_name: Hoffmann, Frank
last_name: Hoffmann
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
- first_name: Ralf
full_name: Mikut, Ralf
last_name: Mikut
file:
- access_level: open_access
content_type: application/pdf
creator: ahetzer
date_created: 2020-05-25T08:01:31Z
date_updated: 2020-05-25T08:01:31Z
file_id: '17060'
file_name: ci_workshop_tornede.pdf
file_size: 468825
relation: main_file
file_date_updated: 2020-05-25T08:01:31Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
page: 135-146
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: Proceedings - 29. Workshop Computational Intelligence, Dortmund, 28.
- 29. November 2019
publication_identifier:
isbn:
- 978-3-7315-0979-0
publication_status: published
publisher: KIT Scientific Publishing, Karlsruhe
status: public
title: 'Algorithm Selection as Recommendation: From Collaborative Filtering to Dyad
Ranking'
type: conference
user_id: '38209'
year: '2019'
...
---
_id: '15013'
author:
- first_name: Klaus
full_name: Brinker, Klaus
last_name: Brinker
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Brinker K, Hüllermeier E. A Reduction of Label Ranking to Multiclass Classification.
In: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge
Discovery in Databases. Würzburg, Germany; 2019.'
apa: Brinker, K., & Hüllermeier, E. (2019). A Reduction of Label Ranking to
Multiclass Classification. In Proceedings ECML/PKDD, European Conference on
Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany.
bibtex: '@inproceedings{Brinker_Hüllermeier_2019, place={Würzburg, Germany}, title={A
Reduction of Label Ranking to Multiclass Classification}, booktitle={Proceedings
ECML/PKDD, European Conference on Machine Learning and Knowledge Discovery in
Databases}, author={Brinker, Klaus and Hüllermeier, Eyke}, year={2019} }'
chicago: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to
Multiclass Classification.” In Proceedings ECML/PKDD, European Conference on
Machine Learning and Knowledge Discovery in Databases. Würzburg, Germany,
2019.
ieee: K. Brinker and E. Hüllermeier, “A Reduction of Label Ranking to Multiclass
Classification,” in Proceedings ECML/PKDD, European Conference on Machine Learning
and Knowledge Discovery in Databases, 2019.
mla: Brinker, Klaus, and Eyke Hüllermeier. “A Reduction of Label Ranking to Multiclass
Classification.” Proceedings ECML/PKDD, European Conference on Machine Learning
and Knowledge Discovery in Databases, 2019.
short: 'K. Brinker, E. Hüllermeier, in: Proceedings ECML/PKDD, European Conference
on Machine Learning and Knowledge Discovery in Databases, Würzburg, Germany, 2019.'
date_created: 2019-11-18T07:26:43Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
language:
- iso: eng
place: Würzburg, Germany
publication: Proceedings ECML/PKDD, European Conference on Machine Learning and Knowledge
Discovery in Databases
status: public
title: A Reduction of Label Ranking to Multiclass Classification
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15014'
author:
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Ines
full_name: Couso, Ines
last_name: Couso
- first_name: Sebastian
full_name: Diestercke, Sebastian
last_name: Diestercke
citation:
ama: 'Hüllermeier E, Couso I, Diestercke S. Learning from Imprecise Data: Adjustments
of Optimistic and Pessimistic Variants. In: Proceedings SUM 2019, International
Conference on Scalable Uncertainty Management. ; 2019.'
apa: 'Hüllermeier, E., Couso, I., & Diestercke, S. (2019). Learning from Imprecise
Data: Adjustments of Optimistic and Pessimistic Variants. In Proceedings SUM
2019, International Conference on Scalable Uncertainty Management.'
bibtex: '@inproceedings{Hüllermeier_Couso_Diestercke_2019, title={Learning from
Imprecise Data: Adjustments of Optimistic and Pessimistic Variants}, booktitle={Proceedings
SUM 2019, International Conference on Scalable Uncertainty Management}, author={Hüllermeier,
Eyke and Couso, Ines and Diestercke, Sebastian}, year={2019} }'
chicago: 'Hüllermeier, Eyke, Ines Couso, and Sebastian Diestercke. “Learning from
Imprecise Data: Adjustments of Optimistic and Pessimistic Variants.” In Proceedings
SUM 2019, International Conference on Scalable Uncertainty Management, 2019.'
ieee: 'E. Hüllermeier, I. Couso, and S. Diestercke, “Learning from Imprecise Data:
Adjustments of Optimistic and Pessimistic Variants,” in Proceedings SUM 2019,
International Conference on Scalable Uncertainty Management, 2019.'
mla: 'Hüllermeier, Eyke, et al. “Learning from Imprecise Data: Adjustments of Optimistic
and Pessimistic Variants.” Proceedings SUM 2019, International Conference on
Scalable Uncertainty Management, 2019.'
short: 'E. Hüllermeier, I. Couso, S. Diestercke, in: Proceedings SUM 2019, International
Conference on Scalable Uncertainty Management, 2019.'
date_created: 2019-11-18T07:38:13Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
language:
- iso: eng
publication: Proceedings SUM 2019, International Conference on Scalable Uncertainty
Management
status: public
title: 'Learning from Imprecise Data: Adjustments of Optimistic and Pessimistic Variants'
type: conference
user_id: '315'
year: '2019'
...
---
_id: '15015'
author:
- first_name: Sascha
full_name: Henzgen, Sascha
last_name: Henzgen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Henzgen S, Hüllermeier E. Mining Rank Data. ACM Transactions on Knowledge
Discovery from Data. 2019:1-36. doi:10.1145/3363572
apa: Henzgen, S., & Hüllermeier, E. (2019). Mining Rank Data. ACM Transactions
on Knowledge Discovery from Data, 1–36. https://doi.org/10.1145/3363572
bibtex: '@article{Henzgen_Hüllermeier_2019, title={Mining Rank Data}, DOI={10.1145/3363572},
journal={ACM Transactions on Knowledge Discovery from Data}, author={Henzgen,
Sascha and Hüllermeier, Eyke}, year={2019}, pages={1–36} }'
chicago: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions
on Knowledge Discovery from Data, 2019, 1–36. https://doi.org/10.1145/3363572.
ieee: S. Henzgen and E. Hüllermeier, “Mining Rank Data,” ACM Transactions on
Knowledge Discovery from Data, pp. 1–36, 2019.
mla: Henzgen, Sascha, and Eyke Hüllermeier. “Mining Rank Data.” ACM Transactions
on Knowledge Discovery from Data, 2019, pp. 1–36, doi:10.1145/3363572.
short: S. Henzgen, E. Hüllermeier, ACM Transactions on Knowledge Discovery from
Data (2019) 1–36.
date_created: 2019-11-18T07:40:27Z
date_updated: 2022-01-06T06:52:14Z
department:
- _id: '34'
- _id: '355'
- _id: '7'
doi: 10.1145/3363572
language:
- iso: eng
page: 1-36
publication: ACM Transactions on Knowledge Discovery from Data
publication_identifier:
issn:
- 1556-4681
publication_status: published
status: public
title: Mining Rank Data
type: journal_article
user_id: '315'
year: '2019'
...
---
_id: '14027'
author:
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Matthias
full_name: Eulert, Matthias
last_name: Eulert
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Eulert M, Holzmann H. Asymptotic confidence sets for the jump curve
in bivariate regression problems. Journal of Multivariate Analysis. 2019:291-312.
doi:10.1016/j.jmva.2019.02.017
apa: Bengs, V., Eulert, M., & Holzmann, H. (2019). Asymptotic confidence sets
for the jump curve in bivariate regression problems. Journal of Multivariate
Analysis, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017
bibtex: '@article{Bengs_Eulert_Holzmann_2019, title={Asymptotic confidence sets
for the jump curve in bivariate regression problems}, DOI={10.1016/j.jmva.2019.02.017},
journal={Journal of Multivariate Analysis}, author={Bengs, Viktor and Eulert,
Matthias and Holzmann, Hajo}, year={2019}, pages={291–312} }'
chicago: Bengs, Viktor, Matthias Eulert, and Hajo Holzmann. “Asymptotic Confidence
Sets for the Jump Curve in Bivariate Regression Problems.” Journal of Multivariate
Analysis, 2019, 291–312. https://doi.org/10.1016/j.jmva.2019.02.017.
ieee: V. Bengs, M. Eulert, and H. Holzmann, “Asymptotic confidence sets for the
jump curve in bivariate regression problems,” Journal of Multivariate Analysis,
pp. 291–312, 2019.
mla: Bengs, Viktor, et al. “Asymptotic Confidence Sets for the Jump Curve in Bivariate
Regression Problems.” Journal of Multivariate Analysis, 2019, pp. 291–312,
doi:10.1016/j.jmva.2019.02.017.
short: V. Bengs, M. Eulert, H. Holzmann, Journal of Multivariate Analysis (2019)
291–312.
date_created: 2019-10-30T14:22:57Z
date_updated: 2022-01-06T06:51:52Z
department:
- _id: '34'
- _id: '355'
doi: 10.1016/j.jmva.2019.02.017
language:
- iso: eng
page: 291-312
publication: Journal of Multivariate Analysis
publication_identifier:
issn:
- 0047-259X
publication_status: published
status: public
title: Asymptotic confidence sets for the jump curve in bivariate regression problems
type: journal_article
user_id: '76599'
year: '2019'
...
---
_id: '14028'
author:
- first_name: Viktor
full_name: Bengs, Viktor
id: '76599'
last_name: Bengs
- first_name: Hajo
full_name: Holzmann, Hajo
last_name: Holzmann
citation:
ama: Bengs V, Holzmann H. Adaptive confidence sets for kink estimation. Electronic
Journal of Statistics. 2019:1523-1579. doi:10.1214/19-ejs1555
apa: Bengs, V., & Holzmann, H. (2019). Adaptive confidence sets for kink estimation.
Electronic Journal of Statistics, 1523–1579. https://doi.org/10.1214/19-ejs1555
bibtex: '@article{Bengs_Holzmann_2019, title={Adaptive confidence sets for kink
estimation}, DOI={10.1214/19-ejs1555},
journal={Electronic Journal of Statistics}, author={Bengs, Viktor and Holzmann,
Hajo}, year={2019}, pages={1523–1579} }'
chicago: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”
Electronic Journal of Statistics, 2019, 1523–79. https://doi.org/10.1214/19-ejs1555.
ieee: V. Bengs and H. Holzmann, “Adaptive confidence sets for kink estimation,”
Electronic Journal of Statistics, pp. 1523–1579, 2019.
mla: Bengs, Viktor, and Hajo Holzmann. “Adaptive Confidence Sets for Kink Estimation.”
Electronic Journal of Statistics, 2019, pp. 1523–79, doi:10.1214/19-ejs1555.
short: V. Bengs, H. Holzmann, Electronic Journal of Statistics (2019) 1523–1579.
date_created: 2019-10-30T14:25:16Z
date_updated: 2022-01-06T06:51:52Z
department:
- _id: '34'
- _id: '355'
doi: 10.1214/19-ejs1555
language:
- iso: eng
page: 1523-1579
publication: Electronic Journal of Statistics
publication_identifier:
issn:
- 1935-7524
publication_status: published
status: public
title: Adaptive confidence sets for kink estimation
type: journal_article
user_id: '76599'
year: '2019'
...
---
_id: '13132'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Tornede A, Hüllermeier E. From Automated to On-The-Fly Machine
Learning. In: INFORMATIK 2019: 50 Jahre Gesellschaft Für Informatik – Informatik
Für Gesellschaft. INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft
für Informatik. Bonn: Gesellschaft für Informatik e.V.; 2019:273-274.'
apa: 'Mohr, F., Wever, M. D., Tornede, A., & Hüllermeier, E. (2019). From Automated
to On-The-Fly Machine Learning. In INFORMATIK 2019: 50 Jahre Gesellschaft für
Informatik – Informatik für Gesellschaft (pp. 273–274). Bonn: Gesellschaft
für Informatik e.V.'
bibtex: '@inproceedings{Mohr_Wever_Tornede_Hüllermeier_2019, place={Bonn}, series={INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik}, title={From
Automated to On-The-Fly Machine Learning}, booktitle={INFORMATIK 2019: 50 Jahre
Gesellschaft für Informatik – Informatik für Gesellschaft}, publisher={Gesellschaft
für Informatik e.V.}, author={Mohr, Felix and Wever, Marcel Dominik and Tornede,
Alexander and Hüllermeier, Eyke}, year={2019}, pages={273–274}, collection={INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft für Informatik} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, Alexander Tornede, and Eyke Hüllermeier.
“From Automated to On-The-Fly Machine Learning.” In INFORMATIK 2019: 50 Jahre
Gesellschaft Für Informatik – Informatik Für Gesellschaft, 273–74. INFORMATIK
2019, Lecture Notes in Informatics (LNI), Gesellschaft Für Informatik. Bonn: Gesellschaft
für Informatik e.V., 2019.'
ieee: 'F. Mohr, M. D. Wever, A. Tornede, and E. Hüllermeier, “From Automated to
On-The-Fly Machine Learning,” in INFORMATIK 2019: 50 Jahre Gesellschaft für
Informatik – Informatik für Gesellschaft, Kassel, 2019, pp. 273–274.'
mla: 'Mohr, Felix, et al. “From Automated to On-The-Fly Machine Learning.” INFORMATIK
2019: 50 Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft,
Gesellschaft für Informatik e.V., 2019, pp. 273–74.'
short: 'F. Mohr, M.D. Wever, A. Tornede, E. Hüllermeier, in: INFORMATIK 2019: 50
Jahre Gesellschaft Für Informatik – Informatik Für Gesellschaft, Gesellschaft
für Informatik e.V., Bonn, 2019, pp. 273–274.'
conference:
end_date: 2019-09-26
location: Kassel
name: Informatik 2019
start_date: 2019-09-23
date_created: 2019-09-04T08:44:46Z
date_updated: 2022-01-06T06:51:28Z
department:
- _id: '355'
language:
- iso: eng
page: ' 273-274 '
place: Bonn
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: 'INFORMATIK 2019: 50 Jahre Gesellschaft für Informatik – Informatik für
Gesellschaft'
publisher: Gesellschaft für Informatik e.V.
series_title: INFORMATIK 2019, Lecture Notes in Informatics (LNI), Gesellschaft für
Informatik
status: public
title: From Automated to On-The-Fly Machine Learning
type: conference_abstract
user_id: '38209'
year: '2019'
...
---
_id: '10232'
abstract:
- lang: eng
text: Existing tools for automated machine learning, such as Auto-WEKA, TPOT, auto-sklearn,
and more recently ML-Plan, have shown impressive results for the tasks of single-label
classification and regression. Yet, there is only little work on other types of
machine learning problems so far. In particular, there is almost no work on automating
the engineering of machine learning solutions for multi-label classification (MLC).
We show how the scope of ML-Plan, an AutoML-tool for multi-class classification,
can be extended towards MLC using MEKA, which is a multi-label extension of the
well-known Java library WEKA. The resulting approach recursively refines MEKA's
multi-label classifiers, nesting other multi-label classifiers for meta algorithms
and single-label classifiers provided by WEKA as base learners. In our evaluation,
we find that the proposed approach yields strong results and performs significantly
better than a set of baselines we compare with.
author:
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Wever MD, Mohr F, Tornede A, Hüllermeier E. Automating Multi-Label Classification
Extending ML-Plan. In: ; 2019.'
apa: Wever, M. D., Mohr, F., Tornede, A., & Hüllermeier, E. (2019). Automating
Multi-Label Classification Extending ML-Plan. Presented at the 6th ICML Workshop
on Automated Machine Learning (AutoML 2019), Long Beach, CA, USA.
bibtex: '@inproceedings{Wever_Mohr_Tornede_Hüllermeier_2019, title={Automating Multi-Label
Classification Extending ML-Plan}, author={Wever, Marcel Dominik and Mohr, Felix
and Tornede, Alexander and Hüllermeier, Eyke}, year={2019} }'
chicago: Wever, Marcel Dominik, Felix Mohr, Alexander Tornede, and Eyke Hüllermeier.
“Automating Multi-Label Classification Extending ML-Plan,” 2019.
ieee: M. D. Wever, F. Mohr, A. Tornede, and E. Hüllermeier, “Automating Multi-Label
Classification Extending ML-Plan,” presented at the 6th ICML Workshop on Automated
Machine Learning (AutoML 2019), Long Beach, CA, USA, 2019.
mla: Wever, Marcel Dominik, et al. Automating Multi-Label Classification Extending
ML-Plan. 2019.
short: 'M.D. Wever, F. Mohr, A. Tornede, E. Hüllermeier, in: 2019.'
conference:
end_date: 2019-06-15
location: Long Beach, CA, USA
name: 6th ICML Workshop on Automated Machine Learning (AutoML 2019)
start_date: 2019-06-09
date_created: 2019-06-11T21:33:06Z
date_updated: 2022-01-06T06:50:33Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2019-09-10T08:19:01Z
date_updated: 2019-09-10T08:20:44Z
file_id: '13177'
file_name: Automating_MultiLabel_Classification_Extending_ML-Plan.pdf
file_size: 388191
relation: main_file
file_date_updated: 2019-09-10T08:20:44Z
has_accepted_license: '1'
language:
- iso: eng
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
status: public
title: Automating Multi-Label Classification Extending ML-Plan
type: conference
user_id: '33176'
year: '2019'
...
---
_id: '20243'
author:
- first_name: Katharina
full_name: Rohlfing, Katharina
id: '50352'
last_name: Rohlfing
- first_name: Giuseppe
full_name: Leonardi, Giuseppe
last_name: Leonardi
- first_name: Iris
full_name: Nomikou, Iris
last_name: Nomikou
- first_name: Joanna
full_name: Rączaszek-Leonardi, Joanna
last_name: Rączaszek-Leonardi
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Rohlfing K, Leonardi G, Nomikou I, Rączaszek-Leonardi J, Hüllermeier E. Multimodal
Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches. IEEE
Transactions on Cognitive and Developmental Systems. Published online 2019.
doi:10.1109/TCDS.2019.2892991'
apa: 'Rohlfing, K., Leonardi, G., Nomikou, I., Rączaszek-Leonardi, J., & Hüllermeier,
E. (2019). Multimodal Turn-Taking: Motivations, Methodological Challenges, and
Novel Approaches. IEEE Transactions on Cognitive and Developmental Systems.
https://doi.org/10.1109/TCDS.2019.2892991'
bibtex: '@article{Rohlfing_Leonardi_Nomikou_Rączaszek-Leonardi_Hüllermeier_2019,
title={Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel
Approaches}, DOI={10.1109/TCDS.2019.2892991},
journal={IEEE Transactions on Cognitive and Developmental Systems}, author={Rohlfing,
Katharina and Leonardi, Giuseppe and Nomikou, Iris and Rączaszek-Leonardi, Joanna
and Hüllermeier, Eyke}, year={2019} }'
chicago: 'Rohlfing, Katharina, Giuseppe Leonardi, Iris Nomikou, Joanna Rączaszek-Leonardi,
and Eyke Hüllermeier. “Multimodal Turn-Taking: Motivations, Methodological Challenges,
and Novel Approaches.” IEEE Transactions on Cognitive and Developmental Systems,
2019. https://doi.org/10.1109/TCDS.2019.2892991.'
ieee: 'K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, and E. Hüllermeier,
“Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel Approaches,”
IEEE Transactions on Cognitive and Developmental Systems, 2019, doi: 10.1109/TCDS.2019.2892991.'
mla: 'Rohlfing, Katharina, et al. “Multimodal Turn-Taking: Motivations, Methodological
Challenges, and Novel Approaches.” IEEE Transactions on Cognitive and Developmental
Systems, 2019, doi:10.1109/TCDS.2019.2892991.'
short: K. Rohlfing, G. Leonardi, I. Nomikou, J. Rączaszek-Leonardi, E. Hüllermeier,
IEEE Transactions on Cognitive and Developmental Systems (2019).
date_created: 2020-11-02T13:25:49Z
date_updated: 2023-02-01T12:39:19Z
department:
- _id: '749'
- _id: '355'
doi: 10.1109/TCDS.2019.2892991
language:
- iso: eng
publication: IEEE Transactions on Cognitive and Developmental Systems
status: public
title: 'Multimodal Turn-Taking: Motivations, Methodological Challenges, and Novel
Approaches'
type: journal_article
user_id: '14931'
year: '2019'
...