---
_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'
...
---
_id: '2479'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Amin
full_name: Faez, Amin
last_name: Faez
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E, Faez A. (WIP) Towards the Automated Composition
of Machine Learning Services. In: SCC. San Francisco, CA, USA: IEEE; 2018.
doi:10.1109/SCC.2018.00039'
apa: 'Mohr, F., Wever, M. D., Hüllermeier, E., & Faez, A. (2018). (WIP) Towards
the Automated Composition of Machine Learning Services. In SCC. San Francisco,
CA, USA: IEEE. https://doi.org/10.1109/SCC.2018.00039'
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_Faez_2018, place={San Francisco,
CA, USA}, title={(WIP) Towards the Automated Composition of Machine Learning Services},
DOI={10.1109/SCC.2018.00039},
booktitle={SCC}, publisher={IEEE}, author={Mohr, Felix and Wever, Marcel Dominik
and Hüllermeier, Eyke and Faez, Amin}, year={2018} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, Eyke Hüllermeier, and Amin Faez. “(WIP)
Towards the Automated Composition of Machine Learning Services.” In SCC.
San Francisco, CA, USA: IEEE, 2018. https://doi.org/10.1109/SCC.2018.00039.'
ieee: F. Mohr, M. D. Wever, E. Hüllermeier, and A. Faez, “(WIP) Towards the Automated
Composition of Machine Learning Services,” in SCC, San Francisco, CA, USA,
2018.
mla: Mohr, Felix, et al. “(WIP) Towards the Automated Composition of Machine Learning
Services.” SCC, IEEE, 2018, doi:10.1109/SCC.2018.00039.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, A. Faez, in: SCC, IEEE, San Francisco,
CA, USA, 2018.'
conference:
end_date: 2018-07-07
location: San Francisco, CA, USA
name: IEEE International Conference on Services Computing, SCC 2018
start_date: 2018-07-02
date_created: 2018-04-24T08:34:52Z
date_updated: 2022-01-06T06:56:35Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1109/SCC.2018.00039
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:08:39Z
date_updated: 2018-11-06T15:08:39Z
file_id: '5382'
file_name: 08456425.pdf
file_size: 237890
relation: main_file
file_date_updated: 2018-11-06T15:08:39Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/document/8456425
oa: '1'
place: San Francisco, CA, USA
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: SCC
publication_status: published
publisher: IEEE
status: public
title: (WIP) Towards the Automated Composition of Machine Learning Services
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '19524'
abstract:
- lang: eng
text: "Object ranking is an important problem in the realm of preference learning.\r\nOn
the basis of training data in the form of a set of rankings of objects,\r\nwhich
are typically represented as feature vectors, the goal is to learn a\r\nranking
function that predicts a linear order of any new set of objects.\r\nCurrent approaches
commonly focus on ranking by scoring, i.e., on learning an\r\nunderlying latent
utility function that seeks to capture the inherent utility\r\nof each object.
These approaches, however, are not able to take possible\r\neffects of context-dependence
into account, where context-dependence means that\r\nthe utility or usefulness
of an object may also depend on what other objects\r\nare available as alternatives.
In this paper, we formalize the problem of\r\ncontext-dependent ranking and present
two general approaches based on two\r\nnatural representations of context-dependent
ranking functions. Both approaches\r\nare instantiated by means of appropriate
neural network architectures, which\r\nare evaluated on suitable benchmark task."
author:
- first_name: Karlson
full_name: Pfannschmidt, Karlson
last_name: Pfannschmidt
- first_name: Pritha
full_name: Gupta, Pritha
last_name: Gupta
- first_name: Eyke
full_name: Hüllermeier, Eyke
last_name: Hüllermeier
citation:
ama: Pfannschmidt K, Gupta P, Hüllermeier E. Deep Architectures for Learning Context-dependent
Ranking Functions. arXiv:180305796. 2018.
apa: Pfannschmidt, K., Gupta, P., & Hüllermeier, E. (2018). Deep Architectures
for Learning Context-dependent Ranking Functions. ArXiv:1803.05796.
bibtex: '@article{Pfannschmidt_Gupta_Hüllermeier_2018, title={Deep Architectures
for Learning Context-dependent Ranking Functions}, journal={arXiv:1803.05796},
author={Pfannschmidt, Karlson and Gupta, Pritha and Hüllermeier, Eyke}, year={2018}
}'
chicago: Pfannschmidt, Karlson, Pritha Gupta, and Eyke Hüllermeier. “Deep Architectures
for Learning Context-Dependent Ranking Functions.” ArXiv:1803.05796, 2018.
ieee: K. Pfannschmidt, P. Gupta, and E. Hüllermeier, “Deep Architectures for Learning
Context-dependent Ranking Functions,” arXiv:1803.05796. 2018.
mla: Pfannschmidt, Karlson, et al. “Deep Architectures for Learning Context-Dependent
Ranking Functions.” ArXiv:1803.05796, 2018.
short: K. Pfannschmidt, P. Gupta, E. Hüllermeier, ArXiv:1803.05796 (2018).
date_created: 2020-09-17T10:53:39Z
date_updated: 2022-01-06T06:54:06Z
department:
- _id: '7'
- _id: '355'
language:
- iso: eng
project:
- _id: '52'
name: Computing Resources Provided by the Paderborn Center for Parallel Computing
publication: arXiv:1803.05796
status: public
title: Deep Architectures for Learning Context-dependent Ranking Functions
type: preprint
user_id: '13472'
year: '2018'
...
---
_id: '2857'
author:
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Theodor
full_name: Lettmann, Theodor
id: '315'
last_name: Lettmann
orcid: 0000-0001-5859-2457
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Mohr F, Lettmann T, Hüllermeier E, Wever MD. Programmatic Task Network Planning.
In: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning. AAAI;
2018:31-39.'
apa: 'Mohr, F., Lettmann, T., Hüllermeier, E., & Wever, M. D. (2018). Programmatic
Task Network Planning. In Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning (pp. 31–39). Delft, Netherlands: AAAI.'
bibtex: '@inproceedings{Mohr_Lettmann_Hüllermeier_Wever_2018, title={Programmatic
Task Network Planning}, booktitle={Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning}, publisher={AAAI}, author={Mohr, Felix and Lettmann, Theodor and Hüllermeier,
Eyke and Wever, Marcel Dominik}, year={2018}, pages={31–39} }'
chicago: Mohr, Felix, Theodor Lettmann, Eyke Hüllermeier, and Marcel Dominik Wever.
“Programmatic Task Network Planning.” In Proceedings of the 1st ICAPS Workshop
on Hierarchical Planning, 31–39. AAAI, 2018.
ieee: F. Mohr, T. Lettmann, E. Hüllermeier, and M. D. Wever, “Programmatic Task
Network Planning,” in Proceedings of the 1st ICAPS Workshop on Hierarchical
Planning, Delft, Netherlands, 2018, pp. 31–39.
mla: Mohr, Felix, et al. “Programmatic Task Network Planning.” Proceedings of
the 1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.
short: 'F. Mohr, T. Lettmann, E. Hüllermeier, M.D. Wever, in: Proceedings of the
1st ICAPS Workshop on Hierarchical Planning, AAAI, 2018, pp. 31–39.'
conference:
end_date: 2018-06-29
location: Delft, Netherlands
name: 28th International Conference on Automated Planning and Scheduling
start_date: 2018-06-24
date_created: 2018-05-24T09:00:20Z
date_updated: 2022-01-06T06:58:08Z
ddc:
- '000'
department:
- _id: '355'
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:18:26Z
date_updated: 2018-11-06T15:18:26Z
file_id: '5384'
file_name: Mohr18ProgrammaticPlanning.pdf
file_size: 349958
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:18:26Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: http://icaps18.icaps-conference.org/fileadmin/alg/conferences/icaps18/workshops/workshop08/docs/Mohr18ProgrammaticPlanning.pdf
oa: '1'
page: 31-39
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: Proceedings of the 1st ICAPS Workshop on Hierarchical Planning
publisher: AAAI
status: public
title: Programmatic Task Network Planning
type: conference
user_id: '315'
year: '2018'
...
---
_id: '24150'
author:
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Shalabh
full_name: Bhatnagar, Shalabh
last_name: Bhatnagar
citation:
ama: Ramaswamy A, Bhatnagar S. Stability of stochastic approximations with “controlled
markov” noise and temporal difference learning. IEEE Transactions on Automatic
Control. 2018;64(6):2614-2620.
apa: Ramaswamy, A., & Bhatnagar, S. (2018). Stability of stochastic approximations
with “controlled markov” noise and temporal difference learning. IEEE Transactions
on Automatic Control, 64(6), 2614–2620.
bibtex: '@article{Ramaswamy_Bhatnagar_2018, title={Stability of stochastic approximations
with “controlled markov” noise and temporal difference learning}, volume={64},
number={6}, journal={IEEE Transactions on Automatic Control}, publisher={IEEE},
author={Ramaswamy, Arunselvan and Bhatnagar, Shalabh}, year={2018}, pages={2614–2620}
}'
chicago: 'Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic
Approximations with ‘Controlled Markov’ Noise and Temporal Difference Learning.”
IEEE Transactions on Automatic Control 64, no. 6 (2018): 2614–20.'
ieee: A. Ramaswamy and S. Bhatnagar, “Stability of stochastic approximations with
‘controlled markov’ noise and temporal difference learning,” IEEE Transactions
on Automatic Control, vol. 64, no. 6, pp. 2614–2620, 2018.
mla: Ramaswamy, Arunselvan, and Shalabh Bhatnagar. “Stability of Stochastic Approximations
with ‘Controlled Markov’ Noise and Temporal Difference Learning.” IEEE Transactions
on Automatic Control, vol. 64, no. 6, IEEE, 2018, pp. 2614–20.
short: A. Ramaswamy, S. Bhatnagar, IEEE Transactions on Automatic Control 64 (2018)
2614–2620.
date_created: 2021-09-10T10:17:54Z
date_updated: 2022-01-06T06:56:08Z
department:
- _id: '355'
intvolume: ' 64'
issue: '6'
language:
- iso: eng
page: 2614-2620
publication: IEEE Transactions on Automatic Control
publisher: IEEE
status: public
title: Stability of stochastic approximations with “controlled markov” noise and temporal
difference learning
type: journal_article
user_id: '66937'
volume: 64
year: '2018'
...
---
_id: '24151'
author:
- first_name: Burak
full_name: Demirel, Burak
last_name: Demirel
- first_name: Arunselvan
full_name: Ramaswamy, Arunselvan
id: '66937'
last_name: Ramaswamy
orcid: https://orcid.org/ 0000-0001-7547-8111
- first_name: Daniel E
full_name: Quevedo, Daniel E
last_name: Quevedo
- first_name: Holger
full_name: Karl, Holger
last_name: Karl
citation:
ama: 'Demirel B, Ramaswamy A, Quevedo DE, Karl H. Deepcas: A deep reinforcement
learning algorithm for control-aware scheduling. IEEE Control Systems Letters.
2018;2(4):737-742.'
apa: 'Demirel, B., Ramaswamy, A., Quevedo, D. E., & Karl, H. (2018). Deepcas:
A deep reinforcement learning algorithm for control-aware scheduling. IEEE
Control Systems Letters, 2(4), 737–742.'
bibtex: '@article{Demirel_Ramaswamy_Quevedo_Karl_2018, title={Deepcas: A deep reinforcement
learning algorithm for control-aware scheduling}, volume={2}, number={4}, journal={IEEE
Control Systems Letters}, publisher={IEEE}, author={Demirel, Burak and Ramaswamy,
Arunselvan and Quevedo, Daniel E and Karl, Holger}, year={2018}, pages={737–742}
}'
chicago: 'Demirel, Burak, Arunselvan Ramaswamy, Daniel E Quevedo, and Holger Karl.
“Deepcas: A Deep Reinforcement Learning Algorithm for Control-Aware Scheduling.”
IEEE Control Systems Letters 2, no. 4 (2018): 737–42.'
ieee: 'B. Demirel, A. Ramaswamy, D. E. Quevedo, and H. Karl, “Deepcas: A deep reinforcement
learning algorithm for control-aware scheduling,” IEEE Control Systems Letters,
vol. 2, no. 4, pp. 737–742, 2018.'
mla: 'Demirel, Burak, et al. “Deepcas: A Deep Reinforcement Learning Algorithm for
Control-Aware Scheduling.” IEEE Control Systems Letters, vol. 2, no. 4,
IEEE, 2018, pp. 737–42.'
short: B. Demirel, A. Ramaswamy, D.E. Quevedo, H. Karl, IEEE Control Systems Letters
2 (2018) 737–742.
date_created: 2021-09-10T10:19:07Z
date_updated: 2022-01-06T06:56:08Z
department:
- _id: '355'
intvolume: ' 2'
issue: '4'
language:
- iso: eng
page: 737-742
publication: IEEE Control Systems Letters
publisher: IEEE
status: public
title: 'Deepcas: A deep reinforcement learning algorithm for control-aware scheduling'
type: journal_article
user_id: '66937'
volume: 2
year: '2018'
...
---
_id: '2471'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. On-The-Fly Service Construction with Prototypes.
In: SCC. San Francisco, CA, USA: IEEE Computer Society; 2018. doi:10.1109/SCC.2018.00036'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). On-The-Fly Service Construction
with Prototypes. In SCC. San Francisco, CA, USA: IEEE Computer Society.
https://doi.org/10.1109/SCC.2018.00036'
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier_2018, place={San Francisco, CA, USA},
title={On-The-Fly Service Construction with Prototypes}, DOI={10.1109/SCC.2018.00036},
booktitle={SCC}, publisher={IEEE Computer Society}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “On-The-Fly Service
Construction with Prototypes.” In SCC. San Francisco, CA, USA: IEEE Computer
Society, 2018. https://doi.org/10.1109/SCC.2018.00036.'
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “On-The-Fly Service Construction
with Prototypes,” in SCC, San Francisco, CA, USA, 2018.
mla: Mohr, Felix, et al. “On-The-Fly Service Construction with Prototypes.” SCC,
IEEE Computer Society, 2018, doi:10.1109/SCC.2018.00036.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: SCC, IEEE Computer Society, San
Francisco, CA, USA, 2018.'
conference:
end_date: 2018-07-07
location: San Francisco, CA, USA
name: IEEE International Conference on Services Computing, SCC 2018
start_date: 2018-07-02
date_created: 2018-04-23T11:40:20Z
date_updated: 2022-01-06T06:56:32Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1109/SCC.2018.00036
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:15:38Z
date_updated: 2018-11-06T15:15:38Z
file_id: '5383'
file_name: 08456422.pdf
file_size: 356132
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:15:38Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://ieeexplore.ieee.org/abstract/document/8456422
oa: '1'
place: San Francisco, CA, USA
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: SCC
publisher: IEEE Computer Society
status: public
title: On-The-Fly Service Construction with Prototypes
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '3402'
abstract:
- lang: eng
text: In machine learning, so-called nested dichotomies are utilized as a reduction
technique, i.e., to decompose a multi-class classification problem into a set
of binary problems, which are solved using a simple binary classifier as a base
learner. The performance of the (multi-class) classifier thus produced strongly
depends on the structure of the decomposition. In this paper, we conduct an empirical
study, in which we compare existing heuristics for selecting a suitable structure
in the form of a nested dichotomy. Moreover, we propose two additional heuristics
as natural completions. One of them is the Best-of-K heuristic, which picks the
(presumably) best among K randomly generated nested dichotomies. Surprisingly,
and in spite of its simplicity, it turns out to outperform the state of the art.
author:
- first_name: Vitalik
full_name: Melnikov, Vitalik
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Melnikov V, Hüllermeier E. On the effectiveness of heuristics for learning
nested dichotomies: an empirical analysis. Machine Learning. 2018. doi:10.1007/s10994-018-5733-1'
apa: 'Melnikov, V., & Hüllermeier, E. (2018). On the effectiveness of heuristics
for learning nested dichotomies: an empirical analysis. Machine Learning.
https://doi.org/10.1007/s10994-018-5733-1'
bibtex: '@article{Melnikov_Hüllermeier_2018, title={On the effectiveness of heuristics
for learning nested dichotomies: an empirical analysis}, DOI={10.1007/s10994-018-5733-1},
journal={Machine Learning}, author={Melnikov, Vitalik and Hüllermeier, Eyke},
year={2018} }'
chicago: 'Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics
for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning,
2018. https://doi.org/10.1007/s10994-018-5733-1.'
ieee: 'V. Melnikov and E. Hüllermeier, “On the effectiveness of heuristics for learning
nested dichotomies: an empirical analysis,” Machine Learning, 2018.'
mla: 'Melnikov, Vitalik, and Eyke Hüllermeier. “On the Effectiveness of Heuristics
for Learning Nested Dichotomies: An Empirical Analysis.” Machine Learning,
2018, doi:10.1007/s10994-018-5733-1.'
short: V. Melnikov, E. Hüllermeier, Machine Learning (2018).
date_created: 2018-06-29T07:44:26Z
date_updated: 2022-01-06T06:59:14Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/s10994-018-5733-1
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:30:57Z
date_updated: 2018-11-02T15:30:57Z
file_id: '5305'
file_name: OnTheEffectivenessOfHeuristics.pdf
file_size: 1482882
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:30:57Z
has_accepted_license: '1'
language:
- iso: eng
project:
- _id: '11'
name: SFB 901 - Subproject B3
- _id: '3'
name: SFB 901 - Project Area B
- _id: '1'
name: SFB 901
publication: Machine Learning
publication_identifier:
issn:
- 1573-0565
status: public
title: 'On the effectiveness of heuristics for learning nested dichotomies: an empirical
analysis'
type: journal_article
user_id: '15504'
year: '2018'
...
---
_id: '3510'
abstract:
- lang: eng
text: Automated machine learning (AutoML) seeks to automatically select, compose,
and parametrize machine learning algorithms, so as to achieve optimal performance
on a given task (dataset). Although current approaches to AutoML have already
produced impressive results, the field is still far from mature, and new techniques
are still being developed. In this paper, we present ML-Plan, a new approach to
AutoML based on hierarchical planning. To highlight the potential of this approach,
we compare ML-Plan to the state-of-the-art frameworks Auto-WEKA, auto-sklearn,
and TPOT. In an extensive series of experiments, we show that ML-Plan is highly
competitive and often outperforms existing approaches.
article_type: original
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. ML-Plan: Automated Machine Learning via Hierarchical
Planning. Machine Learning. Published online 2018:1495-1515. doi:10.1007/s10994-018-5735-z'
apa: 'Mohr, F., Wever, M. D., & Hüllermeier, E. (2018). ML-Plan: Automated Machine
Learning via Hierarchical Planning. Machine Learning, 1495–1515. https://doi.org/10.1007/s10994-018-5735-z'
bibtex: '@article{Mohr_Wever_Hüllermeier_2018, title={ML-Plan: Automated Machine
Learning via Hierarchical Planning}, DOI={10.1007/s10994-018-5735-z},
journal={Machine Learning}, publisher={Springer}, author={Mohr, Felix and Wever,
Marcel Dominik and Hüllermeier, Eyke}, year={2018}, pages={1495–1515} }'
chicago: 'Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “ML-Plan: Automated
Machine Learning via Hierarchical Planning.” Machine Learning, 2018, 1495–1515.
https://doi.org/10.1007/s10994-018-5735-z.'
ieee: 'F. Mohr, M. D. Wever, and E. Hüllermeier, “ML-Plan: Automated Machine Learning
via Hierarchical Planning,” Machine Learning, pp. 1495–1515, 2018, doi:
10.1007/s10994-018-5735-z.'
mla: 'Mohr, Felix, et al. “ML-Plan: Automated Machine Learning via Hierarchical
Planning.” Machine Learning, Springer, 2018, pp. 1495–515, doi:10.1007/s10994-018-5735-z.'
short: F. Mohr, M.D. Wever, E. Hüllermeier, Machine Learning (2018) 1495–1515.
conference:
end_date: 2018-09-14
location: Dublin, Ireland
name: European Conference on Machine Learning and Principles and Practice of Knowledge
Discovery in Databases
start_date: 2018-09-10
date_created: 2018-07-08T14:06:14Z
date_updated: 2022-01-06T06:59:21Z
ddc:
- '000'
department:
- _id: '355'
- _id: '34'
- _id: '7'
- _id: '26'
doi: 10.1007/s10994-018-5735-z
file:
- access_level: closed
content_type: application/pdf
creator: ups
date_created: 2018-11-02T15:32:16Z
date_updated: 2018-11-02T15:32:16Z
file_id: '5306'
file_name: ML-PlanAutomatedMachineLearnin.pdf
file_size: 1070937
relation: main_file
success: 1
file_date_updated: 2018-11-02T15:32:16Z
has_accepted_license: '1'
keyword:
- AutoML
- Hierarchical Planning
- HTN planning
- ML-Plan
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://rdcu.be/3Nc2
oa: '1'
page: 1495-1515
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: Machine Learning
publication_identifier:
eissn:
- 1573-0565
issn:
- 0885-6125
publication_status: epub_ahead
publisher: Springer
status: public
title: 'ML-Plan: Automated Machine Learning via Hierarchical Planning'
type: journal_article
user_id: '5786'
year: '2018'
...
---
_id: '3552'
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: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Mohr F, Wever MD, Hüllermeier E. Reduction Stumps for Multi-Class Classification.
In: Proceedings of the Symposium on Intelligent Data Analysis. ‘s-Hertogenbosch,
the Netherlands. doi:10.1007/978-3-030-01768-2_19'
apa: Mohr, F., Wever, M. D., & Hüllermeier, E. (n.d.). Reduction Stumps for
Multi-Class Classification. In Proceedings of the Symposium on Intelligent
Data Analysis. ‘s-Hertogenbosch, the Netherlands. https://doi.org/10.1007/978-3-030-01768-2_19
bibtex: '@inproceedings{Mohr_Wever_Hüllermeier, place={‘s-Hertogenbosch, the Netherlands},
title={Reduction Stumps for Multi-Class Classification}, DOI={10.1007/978-3-030-01768-2_19},
booktitle={Proceedings of the Symposium on Intelligent Data Analysis}, author={Mohr,
Felix and Wever, Marcel Dominik and Hüllermeier, Eyke} }'
chicago: Mohr, Felix, Marcel Dominik Wever, and Eyke Hüllermeier. “Reduction Stumps
for Multi-Class Classification.” In Proceedings of the Symposium on Intelligent
Data Analysis. ‘s-Hertogenbosch, the Netherlands, n.d. https://doi.org/10.1007/978-3-030-01768-2_19.
ieee: F. Mohr, M. D. Wever, and E. Hüllermeier, “Reduction Stumps for Multi-Class
Classification,” in Proceedings of the Symposium on Intelligent Data Analysis,
‘s-Hertogenbosch, the Netherlands.
mla: Mohr, Felix, et al. “Reduction Stumps for Multi-Class Classification.” Proceedings
of the Symposium on Intelligent Data Analysis, doi:10.1007/978-3-030-01768-2_19.
short: 'F. Mohr, M.D. Wever, E. Hüllermeier, in: Proceedings of the Symposium on
Intelligent Data Analysis, ‘s-Hertogenbosch, the Netherlands, n.d.'
conference:
end_date: 2018-10-26
location: ‘s-Hertogenbosch, the Netherlands
name: Symposium on Intelligent Data Analysis
start_date: 2018-10-24
date_created: 2018-07-13T15:29:15Z
date_updated: 2022-01-06T06:59:25Z
ddc:
- '000'
department:
- _id: '355'
doi: 10.1007/978-3-030-01768-2_19
file:
- access_level: closed
content_type: application/pdf
creator: wever
date_created: 2018-11-06T15:23:02Z
date_updated: 2018-11-06T15:23:02Z
file_id: '5385'
file_name: Mohr2018_Chapter_ReductionStumpsForMulti-classC.pdf
file_size: 1348768
relation: main_file
success: 1
file_date_updated: 2018-11-06T15:23:02Z
has_accepted_license: '1'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://link.springer.com/chapter/10.1007%2F978-3-030-01768-2_19
oa: '1'
place: ‘s-Hertogenbosch, the Netherlands
project:
- _id: '1'
name: SFB 901
- _id: '10'
name: SFB 901 - Subproject B2
- _id: '3'
name: SFB 901 - Project Area B
publication: Proceedings of the Symposium on Intelligent Data Analysis
publication_status: accepted
quality_controlled: '1'
status: public
title: Reduction Stumps for Multi-Class Classification
type: conference
user_id: '49109'
year: '2018'
...
---
_id: '3852'
abstract:
- lang: eng
text: "In automated machine learning (AutoML), the process of engineering machine
learning applications with respect to a specific problem is (partially) automated.\r\nVarious
AutoML tools have already been introduced to provide out-of-the-box machine learning
functionality.\r\nMore specifically, by selecting machine learning algorithms
and optimizing their hyperparameters, these tools produce a machine learning pipeline
tailored to the problem at hand.\r\nExcept for TPOT, all of these tools restrict
the maximum number of processing steps of such a pipeline.\r\nHowever, as TPOT
follows an evolutionary approach, it suffers from performance issues when dealing
with larger datasets.\r\nIn this paper, we present an alternative approach leveraging
a hierarchical planning to configure machine learning pipelines that are unlimited
in length.\r\nWe evaluate our approach and find its performance to be competitive
with other AutoML tools, including TPOT."
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
citation:
ama: 'Wever MD, Mohr F, Hüllermeier E. ML-Plan for Unlimited-Length Machine Learning
Pipelines. In: ICML 2018 AutoML Workshop. ; 2018.'
apa: Wever, M. D., Mohr, F., & Hüllermeier, E. (2018). ML-Plan for Unlimited-Length
Machine Learning Pipelines. In ICML 2018 AutoML Workshop. Stockholm, Sweden.
bibtex: '@inproceedings{Wever_Mohr_Hüllermeier_2018, title={ML-Plan for Unlimited-Length
Machine Learning Pipelines}, booktitle={ICML 2018 AutoML Workshop}, author={Wever,
Marcel Dominik and Mohr, Felix and Hüllermeier, Eyke}, year={2018} }'
chicago: Wever, Marcel Dominik, Felix Mohr, and Eyke Hüllermeier. “ML-Plan for Unlimited-Length
Machine Learning Pipelines.” In ICML 2018 AutoML Workshop, 2018.
ieee: M. D. Wever, F. Mohr, and E. Hüllermeier, “ML-Plan for Unlimited-Length Machine
Learning Pipelines,” in ICML 2018 AutoML Workshop, Stockholm, Sweden, 2018.
mla: Wever, Marcel Dominik, et al. “ML-Plan for Unlimited-Length Machine Learning
Pipelines.” ICML 2018 AutoML Workshop, 2018.
short: 'M.D. Wever, F. Mohr, E. Hüllermeier, in: ICML 2018 AutoML Workshop, 2018.'
conference:
end_date: 2018-07-15
location: Stockholm, Sweden
name: ICML 2018 AutoML Workshop
start_date: 2018-07-10
date_created: 2018-08-09T06:14:54Z
date_updated: 2022-01-06T06:59:46Z
ddc:
- '006'
department:
- _id: '355'
file:
- access_level: open_access
content_type: application/pdf
creator: wever
date_created: 2018-08-09T06:14:43Z
date_updated: 2018-08-09T06:14:43Z
file_id: '3853'
file_name: 38.pdf
file_size: 297811
relation: main_file
file_date_updated: 2018-08-09T06:14:43Z
has_accepted_license: '1'
keyword:
- automated machine learning
- complex pipelines
- hierarchical planning
language:
- iso: eng
main_file_link:
- url: https://docs.google.com/viewer?a=v&pid=sites&srcid=ZGVmYXVsdGRvbWFpbnxhdXRvbWwyMDE4aWNtbHxneDo3M2Q3MjUzYjViNDRhZTAx
oa: '1'
project:
- _id: '1'
name: SFB 901
- _id: '3'
name: SFB 901 - Project Area B
- _id: '10'
name: SFB 901 - Subproject B2
publication: ICML 2018 AutoML Workshop
quality_controlled: '1'
status: public
title: ML-Plan for Unlimited-Length Machine Learning Pipelines
type: conference
urn: '38527'
user_id: '49109'
year: '2018'
...