---
_id: '65182'
abstract:
- lang: eng
text: The aggregation of rating metrics in reputation systems is crucial
for mitigating information overload by condensing customer rating distributions
into singular valence scores. While platforms typically employ technical aggregation
functions, such as the arithmetic mean to capture product quality, it remains
unclear whether these functions align with customers' innate aggregation patterns.
To address this knowledge gap, we designed a controlled economic decision experiment
to elicit customers' aggregation principles by analyzing their product ranking
decisions and contrasting these with various reference functions. Our findings
indicate that, on average, customers aggregate rating information in accordance
with the arithmetic mean. However, a granular analysis at the individual level
reveals significant heterogeneity in aggregation behavior, with a substantial
cluster exhibiting binary patterns that focus equally on negative (1-2 star) and
positive (4-5 star) ratings. Additional clusters concentrate on negative feedback,
particularly 1-star ratings or 1-2 star ratings collectively. Notably, these inherent
aggregation patterns exhibit stability across variations in numerical information
presentation and are not significantly influenced by individual characteristics,
such as online shopping experience, risk attitudes, or demographics. These findings
suggest that while the arithmetic mean captures average consumer behavior, platforms
could benefit from offering customizable aggregation options to better cater to
diverse user preferences for processing rating distributions. By doing so, platforms
can enhance the effectiveness of their reputation systems and improve the overall
quality of decision-making for consumers.
author:
- first_name: Dirk
full_name: van Straaten, Dirk
id: '10311'
last_name: van Straaten
- first_name: Behnud
full_name: Mir Djawadi, Behnud
id: '26032'
last_name: Mir Djawadi
orcid: 0000-0002-6271-5912
- first_name: Vitalik
full_name: Melnikov, Vitalik
id: '58747'
last_name: Melnikov
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: René
full_name: Fahr, René
id: '111'
last_name: Fahr
citation:
ama: van Straaten D, Mir Djawadi B, Melnikov V, Hüllermeier E, Fahr R. Aggregation
Processes in Customer Rating Systems - Insights from an Economic Decision Experiment.
SSRN Electronic Journal. Published online 2026. doi:http://dx.doi.org/10.2139/ssrn.6201258
apa: van Straaten, D., Mir Djawadi, B., Melnikov, V., Hüllermeier, E., & Fahr,
R. (2026). Aggregation Processes in Customer Rating Systems - Insights from an
Economic Decision Experiment. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.6201258
bibtex: '@article{van Straaten_Mir Djawadi_Melnikov_Hüllermeier_Fahr_2026, title={Aggregation
Processes in Customer Rating Systems - Insights from an Economic Decision Experiment},
DOI={http://dx.doi.org/10.2139/ssrn.6201258},
journal={SSRN Electronic Journal}, publisher={Elsevier BV}, author={van Straaten,
Dirk and Mir Djawadi, Behnud and Melnikov, Vitalik and Hüllermeier, Eyke and Fahr,
René}, year={2026} }'
chicago: Straaten, Dirk van, Behnud Mir Djawadi, Vitalik Melnikov, Eyke Hüllermeier,
and René Fahr. “Aggregation Processes in Customer Rating Systems - Insights from
an Economic Decision Experiment.” SSRN Electronic Journal, 2026. http://dx.doi.org/10.2139/ssrn.6201258.
ieee: 'D. van Straaten, B. Mir Djawadi, V. Melnikov, E. Hüllermeier, and R. Fahr,
“Aggregation Processes in Customer Rating Systems - Insights from an Economic
Decision Experiment,” SSRN Electronic Journal, 2026, doi: http://dx.doi.org/10.2139/ssrn.6201258.'
mla: van Straaten, Dirk, et al. “Aggregation Processes in Customer Rating Systems
- Insights from an Economic Decision Experiment.” SSRN Electronic Journal,
Elsevier BV, 2026, doi:http://dx.doi.org/10.2139/ssrn.6201258.
short: D. van Straaten, B. Mir Djawadi, V. Melnikov, E. Hüllermeier, R. Fahr, SSRN
Electronic Journal (2026).
date_created: 2026-03-27T16:21:55Z
date_updated: 2026-03-27T21:55:03Z
department:
- _id: '179'
doi: http://dx.doi.org/10.2139/ssrn.6201258
language:
- iso: eng
publication: SSRN Electronic Journal
publication_status: published
publisher: Elsevier BV
status: public
title: Aggregation Processes in Customer Rating Systems - Insights from an Economic
Decision Experiment
type: journal_article
user_id: '26032'
year: '2026'
...
---
_id: '61234'
abstract:
- lang: eng
text: "The ability to generate explanations that are understood by explainees is
the\r\nquintessence of explainable artificial intelligence. Since understanding\r\ndepends
on the explainee's background and needs, recent research focused on\r\nco-constructive
explanation dialogues, where an explainer continuously monitors\r\nthe explainee's
understanding and adapts their explanations dynamically. We\r\ninvestigate the
ability of large language models (LLMs) to engage as explainers\r\nin co-constructive
explanation dialogues. In particular, we present a user\r\nstudy in which explainees
interact with an LLM in two settings, one of which\r\ninvolves the LLM being instructed
to explain a topic co-constructively. We\r\nevaluate the explainees' understanding
before and after the dialogue, as well\r\nas their perception of the LLMs' co-constructive
behavior. Our results suggest\r\nthat LLMs show some co-constructive behaviors,
such as asking verification\r\nquestions, that foster the explainees' engagement
and can improve understanding\r\nof a topic. However, their ability to effectively
monitor the current\r\nunderstanding and scaffold the explanations accordingly
remains limited."
author:
- first_name: Leandra
full_name: Fichtel, Leandra
last_name: Fichtel
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Patricia
full_name: Jimenez, Patricia
id: '103339'
last_name: Jimenez
- first_name: Nils
full_name: Klowait, Nils
id: '98454'
last_name: Klowait
orcid: 0000-0002-7347-099X
- first_name: Stefan
full_name: Kopp, Stefan
last_name: Kopp
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Amelie
full_name: Robrecht, Amelie
id: '91982'
last_name: Robrecht
orcid: 0000-0001-5622-8248
- first_name: Ingrid
full_name: Scharlau, Ingrid
id: '451'
last_name: Scharlau
orcid: 0000-0003-2364-9489
- first_name: Lutz
full_name: Terfloth, Lutz
id: '37320'
last_name: Terfloth
- first_name: Anna-Lisa
full_name: Vollmer, Anna-Lisa
id: '86589'
last_name: Vollmer
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Fichtel L, Spliethöver M, Hüllermeier E, et al. Investigating Co-Constructive
Behavior of Large Language Models in Explanation Dialogues. In: Proceedings
of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue.
Association for Computational Linguistics.'
apa: Fichtel, L., Spliethöver, M., Hüllermeier, E., Jimenez, P., Klowait, N., Kopp,
S., Ngonga Ngomo, A.-C., Robrecht, A., Scharlau, I., Terfloth, L., Vollmer, A.-L.,
& Wachsmuth, H. (n.d.). Investigating Co-Constructive Behavior of Large Language
Models in Explanation Dialogues. Proceedings of the 26th Annual Meeting of
the Special Interest Group on Discourse and Dialogue. Annual Meeting of the
Special Interest Group on Discourse and Dialogue.
bibtex: '@inproceedings{Fichtel_Spliethöver_Hüllermeier_Jimenez_Klowait_Kopp_Ngonga
Ngomo_Robrecht_Scharlau_Terfloth_et al., place={Avignon, France}, title={Investigating
Co-Constructive Behavior of Large Language Models in Explanation Dialogues},
booktitle={Proceedings of the 26th Annual Meeting of the Special Interest Group
on Discourse and Dialogue}, publisher={Association for Computational Linguistics},
author={Fichtel, Leandra and Spliethöver, Maximilian and Hüllermeier, Eyke and
Jimenez, Patricia and Klowait, Nils and Kopp, Stefan and Ngonga Ngomo, Axel-Cyrille
and Robrecht, Amelie and Scharlau, Ingrid and Terfloth, Lutz and et al.} }'
chicago: 'Fichtel, Leandra, Maximilian Spliethöver, Eyke Hüllermeier, Patricia Jimenez,
Nils Klowait, Stefan Kopp, Axel-Cyrille Ngonga Ngomo, et al. “Investigating Co-Constructive
Behavior of Large Language Models in Explanation Dialogues.” In Proceedings
of the 26th Annual Meeting of the Special Interest Group on Discourse and Dialogue.
Avignon, France: Association for Computational Linguistics, n.d.'
ieee: L. Fichtel et al., “Investigating Co-Constructive Behavior of Large
Language Models in Explanation Dialogues,” presented at the Annual Meeting of
the Special Interest Group on Discourse and Dialogue.
mla: Fichtel, Leandra, et al. “Investigating Co-Constructive Behavior of Large Language
Models in Explanation Dialogues.” Proceedings of the 26th Annual Meeting of
the Special Interest Group on Discourse and Dialogue, Association for Computational
Linguistics.
short: 'L. Fichtel, M. Spliethöver, E. Hüllermeier, P. Jimenez, N. Klowait, S. Kopp,
A.-C. Ngonga Ngomo, A. Robrecht, I. Scharlau, L. Terfloth, A.-L. Vollmer, H. Wachsmuth,
in: Proceedings of the 26th Annual Meeting of the Special Interest Group on Discourse
and Dialogue, Association for Computational Linguistics, Avignon, France, n.d.'
conference:
name: Annual Meeting of the Special Interest Group on Discourse and Dialogue
date_created: 2025-09-11T16:11:17Z
date_updated: 2025-09-12T09:50:48Z
department:
- _id: '660'
external_id:
arxiv:
- '2504.18483'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2504.18483
oa: '1'
place: Avignon, France
project:
- _id: '118'
name: 'TRR 318: Project Area INF'
- _id: '121'
name: 'TRR 318; TP B01: Ein dialogbasierter Ansatz zur Erklärung von Modellen des
maschinellen Lernens'
- _id: '127'
name: 'TRR 318; TP C04: Metaphern als Werkzeug des Erklärens'
- _id: '122'
name: TRR 318 - Subproject B3
- _id: '119'
name: TRR 318 - Project Area Ö
- _id: '114'
name: 'TRR 318; TP A04: Integration des technischen Modells in das Partnermodell
bei der Erklärung von digitalen Artefakten'
publication: Proceedings of the 26th Annual Meeting of the Special Interest Group
on Discourse and Dialogue
publication_status: accepted
publisher: Association for Computational Linguistics
related_material:
link:
- relation: software
url: https://github.com/webis-de/sigdial25-co-constructive-llms
- relation: research_data
url: https://github.com/webis-de/sigdial25-co-constructive-llms-data
status: public
title: Investigating Co-Constructive Behavior of Large Language Models in Explanation
Dialogues
type: conference
user_id: '84035'
year: '2025'
...
---
_id: '59856'
abstract:
- lang: eng
text: Recent advances on instruction fine-tuning have led to the development of
various prompting techniques for large language models, such as explicit reasoning
steps. However, the success of techniques depends on various parameters, such
as the task, language model, and context provided. Finding an effective prompt
is, therefore, often a trial-and-error process. Most existing approaches to automatic
prompting aim to optimize individual techniques instead of compositions of techniques
and their dependence on the input. To fill this gap, we propose an adaptive prompting
approach that predicts the optimal prompt composition ad-hoc for a given input.
We apply our approach to social bias detection, a highly context-dependent task
that requires semantic understanding. We evaluate it with three large language
models on three datasets, comparing compositions to individual techniques and
other baselines. The results underline the importance of finding an effective
prompt composition. Our approach robustly ensures high detection performance,
and is best in several settings. Moreover, first experiments on other tasks support
its generalizability.
author:
- first_name: Maximilian
full_name: Spliethöver, Maximilian
id: '84035'
last_name: Spliethöver
orcid: 0000-0003-4364-1409
- first_name: Tim
full_name: Knebler, Tim
last_name: Knebler
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Henning
full_name: Wachsmuth, Henning
id: '3900'
last_name: Wachsmuth
citation:
ama: 'Spliethöver M, Knebler T, Fumagalli F, et al. Adaptive Prompting: Ad-hoc Prompt
Composition for Social Bias Detection. In: Chiruzzo L, Ritter A, Wang L, eds.
Proceedings of the 2025 Conference of the Nations of the Americas Chapter of
the Association for Computational Linguistics: Human Language Technologies (Volume
1: Long Papers). Association for Computational Linguistics; 2025:2421–2449.'
apa: 'Spliethöver, M., Knebler, T., Fumagalli, F., Muschalik, M., Hammer, B., Hüllermeier,
E., & Wachsmuth, H. (2025). Adaptive Prompting: Ad-hoc Prompt Composition
for Social Bias Detection. In L. Chiruzzo, A. Ritter, & L. Wang (Eds.), Proceedings
of the 2025 Conference of the Nations of the Americas Chapter of the Association
for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
(pp. 2421–2449). Association for Computational Linguistics.'
bibtex: '@inproceedings{Spliethöver_Knebler_Fumagalli_Muschalik_Hammer_Hüllermeier_Wachsmuth_2025,
place={Albuquerque, New Mexico}, title={Adaptive Prompting: Ad-hoc Prompt Composition
for Social Bias Detection}, booktitle={Proceedings of the 2025 Conference of the
Nations of the Americas Chapter of the Association for Computational Linguistics:
Human Language Technologies (Volume 1: Long Papers)}, publisher={Association for
Computational Linguistics}, author={Spliethöver, Maximilian and Knebler, Tim and
Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and Hüllermeier,
Eyke and Wachsmuth, Henning}, editor={Chiruzzo, Luis and Ritter, Alan and Wang,
Lu}, year={2025}, pages={2421–2449} }'
chicago: 'Spliethöver, Maximilian, Tim Knebler, Fabian Fumagalli, Maximilian Muschalik,
Barbara Hammer, Eyke Hüllermeier, and Henning Wachsmuth. “Adaptive Prompting:
Ad-Hoc Prompt Composition for Social Bias Detection.” In Proceedings of the
2025 Conference of the Nations of the Americas Chapter of the Association for
Computational Linguistics: Human Language Technologies (Volume 1: Long Papers),
edited by Luis Chiruzzo, Alan Ritter, and Lu Wang, 2421–2449. Albuquerque, New
Mexico: Association for Computational Linguistics, 2025.'
ieee: 'M. Spliethöver et al., “Adaptive Prompting: Ad-hoc Prompt Composition
for Social Bias Detection,” in Proceedings of the 2025 Conference of the Nations
of the Americas Chapter of the Association for Computational Linguistics: Human
Language Technologies (Volume 1: Long Papers), 2025, pp. 2421–2449.'
mla: 'Spliethöver, Maximilian, et al. “Adaptive Prompting: Ad-Hoc Prompt Composition
for Social Bias Detection.” Proceedings of the 2025 Conference of the Nations
of the Americas Chapter of the Association for Computational Linguistics: Human
Language Technologies (Volume 1: Long Papers), edited by Luis Chiruzzo et
al., Association for Computational Linguistics, 2025, pp. 2421–2449.'
short: 'M. Spliethöver, T. Knebler, F. Fumagalli, M. Muschalik, B. Hammer, E. Hüllermeier,
H. Wachsmuth, in: L. Chiruzzo, A. Ritter, L. Wang (Eds.), Proceedings of the 2025
Conference of the Nations of the Americas Chapter of the Association for Computational
Linguistics: Human Language Technologies (Volume 1: Long Papers), Association
for Computational Linguistics, Albuquerque, New Mexico, 2025, pp. 2421–2449.'
date_created: 2025-05-10T12:37:45Z
date_updated: 2025-09-12T09:51:30Z
department:
- _id: '660'
editor:
- first_name: Luis
full_name: Chiruzzo, Luis
last_name: Chiruzzo
- first_name: Alan
full_name: Ritter, Alan
last_name: Ritter
- first_name: Lu
full_name: Wang, Lu
last_name: Wang
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://aclanthology.org/2025.naacl-long.122/
oa: '1'
page: 2421–2449
place: Albuquerque, New Mexico
project:
- _id: '118'
name: 'TRR 318: Project Area INF'
- _id: '126'
name: TRR 318 - Subproject C3
publication: 'Proceedings of the 2025 Conference of the Nations of the Americas Chapter
of the Association for Computational Linguistics: Human Language Technologies (Volume
1: Long Papers)'
publication_identifier:
isbn:
- 979-8-89176-189-6
publication_status: published
publisher: Association for Computational Linguistics
related_material:
link:
- relation: software
url: https://github.com/webis-de/naacl25-prompt-compositions
status: public
title: 'Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection'
type: conference
user_id: '84035'
year: '2025'
...
---
_id: '61820'
abstract:
- lang: eng
text: "Abstract\r\n A scoring list is
a sequence of simple decision models, where features are incrementally evaluated
and scores of satisfied features are summed to be used for threshold-based decisions
or for calculating class probabilities. In this paper, we introduce a new multi-class
variant and compare it against previously introduced binary classification variants
for incremental decisions, as well as multi-class variants for classical decision-making
using all features. Furthermore, we introduce a new multi-class dataset to assess
collaborative human-machine decision-making, which is suitable for user studies
with non-expert participants. We demonstrate the usefulness of our approach by
evaluating predictive performance and compared to the performance of participants
without AI help."
author:
- first_name: Stefan
full_name: Heid, Stefan
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Jaroslaw
full_name: Kornowicz, Jaroslaw
id: '44029'
last_name: Kornowicz
orcid: 0000-0002-5654-9911
- first_name: Jonas
full_name: Hanselle, Jonas
last_name: Hanselle
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Heid S, Kornowicz J, Hanselle J, Thommes K, Hüllermeier E. MSL: Multi-class
Scoring Lists for Interpretable Incremental Decision-Making. In: Communications
in Computer and Information Science. Springer Nature Switzerland; 2025. doi:10.1007/978-3-032-08327-2_6'
apa: 'Heid, S., Kornowicz, J., Hanselle, J., Thommes, K., & Hüllermeier, E.
(2025). MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making.
In Communications in Computer and Information Science. Springer Nature
Switzerland. https://doi.org/10.1007/978-3-032-08327-2_6'
bibtex: '@inbook{Heid_Kornowicz_Hanselle_Thommes_Hüllermeier_2025, place={Cham},
title={MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making},
DOI={10.1007/978-3-032-08327-2_6},
booktitle={Communications in Computer and Information Science}, publisher={Springer
Nature Switzerland}, author={Heid, Stefan and Kornowicz, Jaroslaw and Hanselle,
Jonas and Thommes, Kirsten and Hüllermeier, Eyke}, year={2025} }'
chicago: 'Heid, Stefan, Jaroslaw Kornowicz, Jonas Hanselle, Kirsten Thommes, and
Eyke Hüllermeier. “MSL: Multi-Class Scoring Lists for Interpretable Incremental
Decision-Making.” In Communications in Computer and Information Science.
Cham: Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-032-08327-2_6.'
ieee: 'S. Heid, J. Kornowicz, J. Hanselle, K. Thommes, and E. Hüllermeier, “MSL:
Multi-class Scoring Lists for Interpretable Incremental Decision-Making,” in Communications
in Computer and Information Science, Cham: Springer Nature Switzerland, 2025.'
mla: 'Heid, Stefan, et al. “MSL: Multi-Class Scoring Lists for Interpretable Incremental
Decision-Making.” Communications in Computer and Information Science, Springer
Nature Switzerland, 2025, doi:10.1007/978-3-032-08327-2_6.'
short: 'S. Heid, J. Kornowicz, J. Hanselle, K. Thommes, E. Hüllermeier, in: Communications
in Computer and Information Science, Springer Nature Switzerland, Cham, 2025.'
date_created: 2025-10-13T13:34:36Z
date_updated: 2025-10-13T13:35:11Z
department:
- _id: '178'
- _id: '184'
doi: 10.1007/978-3-032-08327-2_6
language:
- iso: eng
place: Cham
project:
- _id: '125'
name: TRR 318 - Subproject C2
publication: Communications in Computer and Information Science
publication_identifier:
isbn:
- '9783032083265'
- '9783032083272'
issn:
- 1865-0929
- 1865-0937
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: 'MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making'
type: book_chapter
user_id: '72497'
year: '2025'
...
---
_id: '54911'
article_number: '109190'
author:
- first_name: Stefan
full_name: Heid, Stefan
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Johannes
full_name: Fürnkranz, Johannes
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Heid S, Hanselle JM, Fürnkranz J, Hüllermeier E. Learning decision catalogues
for situated decision making: The case of scoring systems. International Journal
of Approximate Reasoning. 2024;171. doi:10.1016/j.ijar.2024.109190'
apa: 'Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning
decision catalogues for situated decision making: The case of scoring systems.
International Journal of Approximate Reasoning, 171, Article 109190.
https://doi.org/10.1016/j.ijar.2024.109190'
bibtex: '@article{Heid_Hanselle_Fürnkranz_Hüllermeier_2024, title={Learning decision
catalogues for situated decision making: The case of scoring systems}, volume={171},
DOI={10.1016/j.ijar.2024.109190},
number={109190}, journal={International Journal of Approximate Reasoning}, publisher={Elsevier
BV}, author={Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and
Hüllermeier, Eyke}, year={2024} }'
chicago: 'Heid, Stefan, Jonas Manuel Hanselle, Johannes Fürnkranz, and Eyke Hüllermeier.
“Learning Decision Catalogues for Situated Decision Making: The Case of Scoring
Systems.” International Journal of Approximate Reasoning 171 (2024). https://doi.org/10.1016/j.ijar.2024.109190.'
ieee: 'S. Heid, J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Learning decision
catalogues for situated decision making: The case of scoring systems,” International
Journal of Approximate Reasoning, vol. 171, Art. no. 109190, 2024, doi: 10.1016/j.ijar.2024.109190.'
mla: 'Heid, Stefan, et al. “Learning Decision Catalogues for Situated Decision Making:
The Case of Scoring Systems.” International Journal of Approximate Reasoning,
vol. 171, 109190, Elsevier BV, 2024, doi:10.1016/j.ijar.2024.109190.'
short: S. Heid, J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, International Journal
of Approximate Reasoning 171 (2024).
date_created: 2024-06-26T14:28:07Z
date_updated: 2024-06-26T14:28:47Z
department:
- _id: '660'
doi: 10.1016/j.ijar.2024.109190
intvolume: ' 171'
language:
- iso: eng
project:
- _id: '125'
name: 'TRR 318 - C2: TRR 318 - Subproject C2'
publication: International Journal of Approximate Reasoning
publication_identifier:
issn:
- 0888-613X
publication_status: published
publisher: Elsevier BV
status: public
title: 'Learning decision catalogues for situated decision making: The case of scoring
systems'
type: journal_article
user_id: '72497'
volume: 171
year: '2024'
...
---
_id: '54910'
article_number: '109190'
author:
- first_name: Stefan
full_name: Heid, Stefan
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Johannes
full_name: Fürnkranz, Johannes
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Heid S, Hanselle JM, Fürnkranz J, Hüllermeier E. Learning decision catalogues
for situated decision making: The case of scoring systems. International Journal
of Approximate Reasoning. 2024;171. doi:10.1016/j.ijar.2024.109190'
apa: 'Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning
decision catalogues for situated decision making: The case of scoring systems.
International Journal of Approximate Reasoning, 171, Article 109190.
https://doi.org/10.1016/j.ijar.2024.109190'
bibtex: '@article{Heid_Hanselle_Fürnkranz_Hüllermeier_2024, title={Learning decision
catalogues for situated decision making: The case of scoring systems}, volume={171},
DOI={10.1016/j.ijar.2024.109190},
number={109190}, journal={International Journal of Approximate Reasoning}, publisher={Elsevier
BV}, author={Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and
Hüllermeier, Eyke}, year={2024} }'
chicago: 'Heid, Stefan, Jonas Manuel Hanselle, Johannes Fürnkranz, and Eyke Hüllermeier.
“Learning Decision Catalogues for Situated Decision Making: The Case of Scoring
Systems.” International Journal of Approximate Reasoning 171 (2024). https://doi.org/10.1016/j.ijar.2024.109190.'
ieee: 'S. Heid, J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Learning decision
catalogues for situated decision making: The case of scoring systems,” International
Journal of Approximate Reasoning, vol. 171, Art. no. 109190, 2024, doi: 10.1016/j.ijar.2024.109190.'
mla: 'Heid, Stefan, et al. “Learning Decision Catalogues for Situated Decision Making:
The Case of Scoring Systems.” International Journal of Approximate Reasoning,
vol. 171, 109190, Elsevier BV, 2024, doi:10.1016/j.ijar.2024.109190.'
short: S. Heid, J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, International Journal
of Approximate Reasoning 171 (2024).
date_created: 2024-06-26T14:27:27Z
date_updated: 2024-06-26T14:27:42Z
department:
- _id: '178'
- _id: '184'
doi: 10.1016/j.ijar.2024.109190
intvolume: ' 171'
language:
- iso: eng
publication: International Journal of Approximate Reasoning
publication_identifier:
issn:
- 0888-613X
publication_status: published
publisher: Elsevier BV
status: public
title: 'Learning decision catalogues for situated decision making: The case of scoring
systems'
type: journal_article
user_id: '72497'
volume: 171
year: '2024'
...
---
_id: '54907'
article_number: '109190'
author:
- first_name: Stefan
full_name: Heid, Stefan
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Johannes
full_name: Fürnkranz, Johannes
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Heid S, Hanselle JM, Fürnkranz J, Hüllermeier E. Learning decision catalogues
for situated decision making: The case of scoring systems. International Journal
of Approximate Reasoning. 2024;171. doi:10.1016/j.ijar.2024.109190'
apa: 'Heid, S., Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2024). Learning
decision catalogues for situated decision making: The case of scoring systems.
International Journal of Approximate Reasoning, 171, Article 109190.
https://doi.org/10.1016/j.ijar.2024.109190'
bibtex: '@article{Heid_Hanselle_Fürnkranz_Hüllermeier_2024, title={Learning decision
catalogues for situated decision making: The case of scoring systems}, volume={171},
DOI={10.1016/j.ijar.2024.109190},
number={109190}, journal={International Journal of Approximate Reasoning}, publisher={Elsevier
BV}, author={Heid, Stefan and Hanselle, Jonas Manuel and Fürnkranz, Johannes and
Hüllermeier, Eyke}, year={2024} }'
chicago: 'Heid, Stefan, Jonas Manuel Hanselle, Johannes Fürnkranz, and Eyke Hüllermeier.
“Learning Decision Catalogues for Situated Decision Making: The Case of Scoring
Systems.” International Journal of Approximate Reasoning 171 (2024). https://doi.org/10.1016/j.ijar.2024.109190.'
ieee: 'S. Heid, J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Learning decision
catalogues for situated decision making: The case of scoring systems,” International
Journal of Approximate Reasoning, vol. 171, Art. no. 109190, 2024, doi: 10.1016/j.ijar.2024.109190.'
mla: 'Heid, Stefan, et al. “Learning Decision Catalogues for Situated Decision Making:
The Case of Scoring Systems.” International Journal of Approximate Reasoning,
vol. 171, 109190, Elsevier BV, 2024, doi:10.1016/j.ijar.2024.109190.'
short: S. Heid, J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, International Journal
of Approximate Reasoning 171 (2024).
date_created: 2024-06-26T14:19:19Z
date_updated: 2024-11-20T09:44:54Z
department:
- _id: '660'
doi: 10.1016/j.ijar.2024.109190
intvolume: ' 171'
language:
- iso: eng
project:
- _id: '125'
name: 'TRR 318 - C2: TRR 318 - Subproject C2'
publication: International Journal of Approximate Reasoning
publication_identifier:
issn:
- 0888-613X
publication_status: published
publisher: Elsevier BV
status: public
title: 'Learning decision catalogues for situated decision making: The case of scoring
systems'
type: journal_article
user_id: '72497'
volume: 171
year: '2024'
...
---
_id: '57645'
author:
- first_name: Stefan
full_name: Heid, Stefan
id: '39640'
last_name: Heid
orcid: 0000-0002-9461-7372
- first_name: Jaroslaw
full_name: Kornowicz, Jaroslaw
id: '44029'
last_name: Kornowicz
orcid: 0000-0002-5654-9911
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Kirsten
full_name: Thommes, Kirsten
id: '72497'
last_name: Thommes
citation:
ama: 'Heid S, Kornowicz J, Hanselle JM, Hüllermeier E, Thommes K. Human-AI Co-Construction
of Interpretable Predictive Models: The Case of Scoring Systems. In: PROCEEDINGS
34. WORKSHOP COMPUTATIONAL INTELLIGENCE. Vol 21. ; 2024:233.'
apa: 'Heid, S., Kornowicz, J., Hanselle, J. M., Hüllermeier, E., & Thommes,
K. (2024). Human-AI Co-Construction of Interpretable Predictive Models: The Case
of Scoring Systems. PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE,
21, 233.'
bibtex: '@inproceedings{Heid_Kornowicz_Hanselle_Hüllermeier_Thommes_2024, title={Human-AI
Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems},
volume={21}, booktitle={PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE},
author={Heid, Stefan and Kornowicz, Jaroslaw and Hanselle, Jonas Manuel and Hüllermeier,
Eyke and Thommes, Kirsten}, year={2024}, pages={233} }'
chicago: 'Heid, Stefan, Jaroslaw Kornowicz, Jonas Manuel Hanselle, Eyke Hüllermeier,
and Kirsten Thommes. “Human-AI Co-Construction of Interpretable Predictive Models:
The Case of Scoring Systems.” In PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE,
21:233, 2024.'
ieee: 'S. Heid, J. Kornowicz, J. M. Hanselle, E. Hüllermeier, and K. Thommes, “Human-AI
Co-Construction of Interpretable Predictive Models: The Case of Scoring Systems,”
in PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 2024, vol. 21,
p. 233.'
mla: 'Heid, Stefan, et al. “Human-AI Co-Construction of Interpretable Predictive
Models: The Case of Scoring Systems.” PROCEEDINGS 34. WORKSHOP COMPUTATIONAL
INTELLIGENCE, vol. 21, 2024, p. 233.'
short: 'S. Heid, J. Kornowicz, J.M. Hanselle, E. Hüllermeier, K. Thommes, in: PROCEEDINGS
34. WORKSHOP COMPUTATIONAL INTELLIGENCE, 2024, p. 233.'
date_created: 2024-12-09T08:05:54Z
date_updated: 2024-12-09T08:06:37Z
department:
- _id: '178'
- _id: '184'
intvolume: ' 21'
language:
- iso: eng
page: '233'
project:
- _id: '125'
name: 'TRR 318 - C2: TRR 318 - Subproject C2'
publication: PROCEEDINGS 34. WORKSHOP COMPUTATIONAL INTELLIGENCE
status: public
title: 'Human-AI Co-Construction of Interpretable Predictive Models: The Case of Scoring
Systems'
type: conference
user_id: '72497'
volume: 21
year: '2024'
...
---
_id: '55631'
abstract:
- lang: eng
text: This paper investigates the remaining useful lifetime (RUL) estimation of
bearings under dynamic, i.e., time-varying, operating conditions (OC). Unlike
conventional studies that assume constant OC in bearing accelerated life tests,
we introduce a dataset with time-varying OC during run-to-failure experiments,
simulating real-world scenarios. We explore data-driven approaches to identify
the transition point from a healthy to an unhealthy state and estimate the RUL.
Additionally, we examine strategies for integrating OC information to enhance
RUL estimations. These methodologies are evaluated through numerical experiments
using various machine learning algorithms.
article_number: '9'
author:
- first_name: Alireza
full_name: Javanmardi, Alireza
last_name: Javanmardi
- first_name: Osarenren Kennedy
full_name: Aimiyekagbon, Osarenren Kennedy
id: '9557'
last_name: Aimiyekagbon
- first_name: Amelie
full_name: Bender, Amelie
id: '54290'
last_name: Bender
- first_name: James Kuria
full_name: Kimotho, James Kuria
last_name: Kimotho
- first_name: Walter
full_name: Sextro, Walter
id: '21220'
last_name: Sextro
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Javanmardi A, Aimiyekagbon OK, Bender A, Kimotho JK, Sextro W, Hüllermeier
E. Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying
Conditions. In: PHM Society European Conference. Vol 8. PHM Society; 2024.
doi:10.36001/phme.2024.v8i1.4101'
apa: Javanmardi, A., Aimiyekagbon, O. K., Bender, A., Kimotho, J. K., Sextro, W.,
& Hüllermeier, E. (2024). Remaining Useful Lifetime Estimation of Bearings
Operating under Time-Varying Conditions. PHM Society European Conference,
8(1), Article 9. https://doi.org/10.36001/phme.2024.v8i1.4101
bibtex: '@inproceedings{Javanmardi_Aimiyekagbon_Bender_Kimotho_Sextro_Hüllermeier_2024,
title={Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying
Conditions}, volume={8}, DOI={10.36001/phme.2024.v8i1.4101},
number={19}, booktitle={PHM Society European Conference}, publisher={PHM Society},
author={Javanmardi, Alireza and Aimiyekagbon, Osarenren Kennedy and Bender, Amelie
and Kimotho, James Kuria and Sextro, Walter and Hüllermeier, Eyke}, year={2024}
}'
chicago: Javanmardi, Alireza, Osarenren Kennedy Aimiyekagbon, Amelie Bender, James
Kuria Kimotho, Walter Sextro, and Eyke Hüllermeier. “Remaining Useful Lifetime
Estimation of Bearings Operating under Time-Varying Conditions.” In PHM Society
European Conference, Vol. 8. PHM Society, 2024. https://doi.org/10.36001/phme.2024.v8i1.4101.
ieee: 'A. Javanmardi, O. K. Aimiyekagbon, A. Bender, J. K. Kimotho, W. Sextro, and
E. Hüllermeier, “Remaining Useful Lifetime Estimation of Bearings Operating under
Time-Varying Conditions,” in PHM Society European Conference, Prague, Czech
Republic, 2024, vol. 8, no. 1, doi: 10.36001/phme.2024.v8i1.4101.'
mla: Javanmardi, Alireza, et al. “Remaining Useful Lifetime Estimation of Bearings
Operating under Time-Varying Conditions.” PHM Society European Conference,
vol. 8, no. 1, 9, PHM Society, 2024, doi:10.36001/phme.2024.v8i1.4101.
short: 'A. Javanmardi, O.K. Aimiyekagbon, A. Bender, J.K. Kimotho, W. Sextro, E.
Hüllermeier, in: PHM Society European Conference, PHM Society, 2024.'
conference:
end_date: 2024-07-05
location: Prague, Czech Republic
name: 8th European Conference of the Prognostics and Health Management Society 2024
start_date: 2024-07-03
date_created: 2024-08-19T07:41:32Z
date_updated: 2025-02-10T10:37:52Z
department:
- _id: '151'
doi: 10.36001/phme.2024.v8i1.4101
intvolume: ' 8'
issue: '1'
language:
- iso: eng
publication: PHM Society European Conference
publication_identifier:
isbn:
- 978-1-936263-40-0
publication_status: published
publisher: PHM Society
quality_controlled: '1'
status: public
title: Remaining Useful Lifetime Estimation of Bearings Operating under Time-Varying
Conditions
type: conference
user_id: '9557'
volume: 8
year: '2024'
...
---
_id: '53073'
abstract:
- lang: eng
text: While shallow decision trees may be interpretable, larger ensemble models
like gradient-boosted trees, which often set the state of the art in machine learning
problems involving tabular data, still remain black box models. As a remedy, the
Shapley value (SV) is a well-known concept in explainable artificial intelligence
(XAI) research for quantifying additive feature attributions of predictions. The
model-specific TreeSHAP methodology solves the exponential complexity for retrieving
exact SVs from tree-based models. Expanding beyond individual feature attribution,
Shapley interactions reveal the impact of intricate feature interactions of any
order. In this work, we present TreeSHAP-IQ, an efficient method to compute any-order
additive Shapley interactions for predictions of tree-based models. TreeSHAP-IQ
is supported by a mathematical framework that exploits polynomial arithmetic to
compute the interaction scores in a single recursive traversal of the tree, akin
to Linear TreeSHAP. We apply TreeSHAP-IQ on state-of-the-art tree ensembles and
explore interactions on well-established benchmark datasets.
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: 'Muschalik M, Fumagalli F, Hammer B, Huellermeier E. Beyond TreeSHAP: Efficient
Computation of Any-Order Shapley Interactions for Tree Ensembles. In: Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI). Vol 38. ; 2024:14388-14396.
doi:10.1609/aaai.v38i13.29352'
apa: 'Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier, E. (2024). Beyond
TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles.
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 38(13),
14388–14396. https://doi.org/10.1609/aaai.v38i13.29352'
bibtex: '@inproceedings{Muschalik_Fumagalli_Hammer_Huellermeier_2024, title={Beyond
TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles},
volume={38}, DOI={10.1609/aaai.v38i13.29352},
number={13}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI)}, author={Muschalik, Maximilian and Fumagalli, Fabian and Hammer, Barbara
and Huellermeier, Eyke}, year={2024}, pages={14388–14396} }'
chicago: 'Muschalik, Maximilian, Fabian Fumagalli, Barbara Hammer, and Eyke Huellermeier.
“Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for
Tree Ensembles.” In Proceedings of the AAAI Conference on Artificial Intelligence
(AAAI), 38:14388–96, 2024. https://doi.org/10.1609/aaai.v38i13.29352.'
ieee: 'M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier, “Beyond TreeSHAP:
Efficient Computation of Any-Order Shapley Interactions for Tree Ensembles,” in
Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2024,
vol. 38, no. 13, pp. 14388–14396, doi: 10.1609/aaai.v38i13.29352.'
mla: 'Muschalik, Maximilian, et al. “Beyond TreeSHAP: Efficient Computation of Any-Order
Shapley Interactions for Tree Ensembles.” Proceedings of the AAAI Conference
on Artificial Intelligence (AAAI), vol. 38, no. 13, 2024, pp. 14388–96, doi:10.1609/aaai.v38i13.29352.'
short: 'M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in: Proceedings
of the AAAI Conference on Artificial Intelligence (AAAI), 2024, pp. 14388–14396.'
date_created: 2024-03-27T14:50:04Z
date_updated: 2025-09-11T16:20:11Z
department:
- _id: '660'
doi: 10.1609/aaai.v38i13.29352
intvolume: ' 38'
issue: '13'
keyword:
- Explainable Artificial Intelligence
language:
- iso: eng
page: 14388-14396
project:
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
publication: Proceedings of the AAAI Conference on Artificial Intelligence (AAAI)
publication_identifier:
issn:
- 2374-3468
- 2159-5399
publication_status: published
status: public
title: 'Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for
Tree Ensembles'
type: conference
user_id: '93420'
volume: 38
year: '2024'
...
---
_id: '55311'
abstract:
- lang: eng
text: Addressing the limitations of individual attribution scores via the Shapley
value (SV), the field of explainable AI (XAI) has recently explored intricate
interactions of features or data points. In particular, extensions of the SV,
such as the Shapley Interaction Index (SII), have been proposed as a measure to
still benefit from the axiomatic basis of the SV. However, similar to the SV,
their exact computation remains computationally prohibitive. Hence, we propose
with SVARM-IQ a sampling-based approach to efficiently approximate Shapley-based
interaction indices of any order. SVARM-IQ can be applied to a broad class of
interaction indices, including the SII, by leveraging a novel stratified representation.
We provide non-asymptotic theoretical guarantees on its approximation quality
and empirically demonstrate that SVARM-IQ achieves state-of-the-art estimation
results in practical XAI scenarios on different model classes and application
domains.
author:
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: 'Kolpaczki P, Muschalik M, Fumagalli F, Hammer B, Huellermeier E. SVARM-IQ:
Efficient Approximation of Any-order Shapley Interactions through Stratification.
In: Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS). Vol 238. Proceedings of Machine Learning Research.
PMLR; 2024:3520–3528.'
apa: 'Kolpaczki, P., Muschalik, M., Fumagalli, F., Hammer, B., & Huellermeier,
E. (2024). SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions
through Stratification. Proceedings of The 27th International Conference on
Artificial Intelligence and Statistics (AISTATS), 238, 3520–3528.'
bibtex: '@inproceedings{Kolpaczki_Muschalik_Fumagalli_Hammer_Huellermeier_2024,
series={Proceedings of Machine Learning Research}, title={SVARM-IQ: Efficient
Approximation of Any-order Shapley Interactions through Stratification}, volume={238},
booktitle={Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS)}, publisher={PMLR}, author={Kolpaczki, Patrick and Muschalik,
Maximilian and Fumagalli, Fabian and Hammer, Barbara and Huellermeier, Eyke},
year={2024}, pages={3520–3528}, collection={Proceedings of Machine Learning Research}
}'
chicago: 'Kolpaczki, Patrick, Maximilian Muschalik, Fabian Fumagalli, Barbara Hammer,
and Eyke Huellermeier. “SVARM-IQ: Efficient Approximation of Any-Order Shapley
Interactions through Stratification.” In Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics (AISTATS), 238:3520–3528.
Proceedings of Machine Learning Research. PMLR, 2024.'
ieee: 'P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, and E. Huellermeier,
“SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through Stratification,”
in Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS), 2024, vol. 238, pp. 3520–3528.'
mla: 'Kolpaczki, Patrick, et al. “SVARM-IQ: Efficient Approximation of Any-Order
Shapley Interactions through Stratification.” Proceedings of The 27th International
Conference on Artificial Intelligence and Statistics (AISTATS), vol. 238,
PMLR, 2024, pp. 3520–3528.'
short: 'P. Kolpaczki, M. Muschalik, F. Fumagalli, B. Hammer, E. Huellermeier, in:
Proceedings of The 27th International Conference on Artificial Intelligence and
Statistics (AISTATS), PMLR, 2024, pp. 3520–3528.'
date_created: 2024-07-18T09:39:14Z
date_updated: 2025-09-11T16:22:30Z
department:
- _id: '660'
intvolume: ' 238'
language:
- iso: eng
page: 3520–3528
project:
- _id: '109'
name: 'TRR 318: TRR 318 - Erklärbarkeit konstruieren'
- _id: '117'
name: 'TRR 318 - C: TRR 318 - Project Area C'
- _id: '126'
name: 'TRR 318 - C3: TRR 318 - Subproject C3'
publication: Proceedings of The 27th International Conference on Artificial Intelligence
and Statistics (AISTATS)
publisher: PMLR
series_title: Proceedings of Machine Learning Research
status: public
title: 'SVARM-IQ: Efficient Approximation of Any-order Shapley Interactions through
Stratification'
type: conference
user_id: '93420'
volume: 238
year: '2024'
...
---
_id: '61228'
author:
- first_name: Maximilian
full_name: Muschalik, Maximilian
last_name: Muschalik
- first_name: Hubert
full_name: Baniecki, Hubert
last_name: Baniecki
- first_name: Fabian
full_name: Fumagalli, Fabian
id: '93420'
last_name: Fumagalli
- first_name: Patrick
full_name: Kolpaczki, Patrick
last_name: Kolpaczki
- first_name: Barbara
full_name: Hammer, Barbara
last_name: Hammer
- first_name: Eyke
full_name: Huellermeier, Eyke
id: '48129'
last_name: Huellermeier
citation:
ama: 'Muschalik M, Baniecki H, Fumagalli F, Kolpaczki P, Hammer B, Huellermeier
E. shapiq: Shapley interactions for machine learning. In: Advances in Neural
Information Processing Systems (NeurIPS). Vol 37. ; 2024:130324–130357.'
apa: 'Muschalik, M., Baniecki, H., Fumagalli, F., Kolpaczki, P., Hammer, B., &
Huellermeier, E. (2024). shapiq: Shapley interactions for machine learning. Advances
in Neural Information Processing Systems (NeurIPS), 37, 130324–130357.'
bibtex: '@inproceedings{Muschalik_Baniecki_Fumagalli_Kolpaczki_Hammer_Huellermeier_2024,
title={shapiq: Shapley interactions for machine learning}, volume={37}, booktitle={Advances
in Neural Information Processing Systems (NeurIPS)}, author={Muschalik, Maximilian
and Baniecki, Hubert and Fumagalli, Fabian and Kolpaczki, Patrick and Hammer,
Barbara and Huellermeier, Eyke}, year={2024}, pages={130324–130357} }'
chicago: 'Muschalik, Maximilian, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki,
Barbara Hammer, and Eyke Huellermeier. “Shapiq: Shapley Interactions for Machine
Learning.” In Advances in Neural Information Processing Systems (NeurIPS),
37:130324–130357, 2024.'
ieee: 'M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, and E.
Huellermeier, “shapiq: Shapley interactions for machine learning,” in Advances
in Neural Information Processing Systems (NeurIPS), 2024, vol. 37, pp. 130324–130357.'
mla: 'Muschalik, Maximilian, et al. “Shapiq: Shapley Interactions for Machine Learning.”
Advances in Neural Information Processing Systems (NeurIPS), vol. 37, 2024,
pp. 130324–130357.'
short: 'M. Muschalik, H. Baniecki, F. Fumagalli, P. Kolpaczki, B. Hammer, E. Huellermeier,
in: Advances in Neural Information Processing Systems (NeurIPS), 2024, pp. 130324–130357.'
date_created: 2025-09-11T15:39:01Z
date_updated: 2025-09-11T16:17:35Z
department:
- _id: '660'
intvolume: ' 37'
language:
- iso: eng
page: 130324–130357
project:
- _id: '117'
name: TRR 318 - Project Area C
- _id: '126'
name: TRR 318 - Subproject C3
- _id: '109'
name: 'TRR 318: Erklärbarkeit konstruieren'
publication: Advances in Neural Information Processing Systems (NeurIPS)
status: public
title: 'shapiq: Shapley interactions for machine learning'
type: conference
user_id: '93420'
volume: 37
year: '2024'
...
---
_id: '51373'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Johannes
full_name: Fürnkranz, Johannes
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Fürnkranz J, Hüllermeier E. Probabilistic Scoring Lists for Interpretable
Machine Learning. In: 26th International Conference on Discovery Science .
Vol 14050. Lecture Notes in Computer Science. Springer Nature Switzerland; 2023:189-203.
doi:10.1007/978-3-031-45275-8_13'
apa: Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic
Scoring Lists for Interpretable Machine Learning. 26th International Conference
on Discovery Science , 14050, 189–203. https://doi.org/10.1007/978-3-031-45275-8_13
bibtex: '@inproceedings{Hanselle_Fürnkranz_Hüllermeier_2023, place={Cham}, series={Lecture
Notes in Computer Science}, title={Probabilistic Scoring Lists for Interpretable
Machine Learning}, volume={14050}, DOI={10.1007/978-3-031-45275-8_13},
booktitle={26th International Conference on Discovery Science }, publisher={Springer
Nature Switzerland}, author={Hanselle, Jonas Manuel and Fürnkranz, Johannes and
Hüllermeier, Eyke}, year={2023}, pages={189–203}, collection={Lecture Notes in
Computer Science} }'
chicago: 'Hanselle, Jonas Manuel, Johannes Fürnkranz, and Eyke Hüllermeier. “Probabilistic
Scoring Lists for Interpretable Machine Learning.” In 26th International Conference
on Discovery Science , 14050:189–203. Lecture Notes in Computer Science. Cham:
Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-45275-8_13.'
ieee: 'J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Probabilistic Scoring
Lists for Interpretable Machine Learning,” in 26th International Conference
on Discovery Science , Porto, 2023, vol. 14050, pp. 189–203, doi: 10.1007/978-3-031-45275-8_13.'
mla: Hanselle, Jonas Manuel, et al. “Probabilistic Scoring Lists for Interpretable
Machine Learning.” 26th International Conference on Discovery Science ,
vol. 14050, Springer Nature Switzerland, 2023, pp. 189–203, doi:10.1007/978-3-031-45275-8_13.
short: 'J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, in: 26th International Conference
on Discovery Science , Springer Nature Switzerland, Cham, 2023, pp. 189–203.'
conference:
end_date: 2021-10-11
location: Porto
name: '26th International Conference on Discovery Science '
start_date: 2023-10-9
date_created: 2024-02-18T11:05:55Z
date_updated: 2024-02-26T08:41:49Z
doi: 10.1007/978-3-031-45275-8_13
intvolume: ' 14050'
language:
- iso: eng
page: 189-203
place: Cham
publication: '26th International Conference on Discovery Science '
publication_identifier:
isbn:
- '9783031452741'
- '9783031452758'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
series_title: Lecture Notes in Computer Science
status: public
title: Probabilistic Scoring Lists for Interpretable Machine Learning
type: conference
user_id: '54779'
volume: 14050
year: '2023'
...
---
_id: '54613'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In:
Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly
Computing – Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe
des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85–104.
doi:10.5281/zenodo.8068466'
apa: Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M.,
Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.),
On-The-Fly Computing – Individualized IT-services in dynamic markets (Vol.
412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
bibtex: '@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023,
series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Configuration
and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466},
booktitle={On-The-Fly Computing – Individualized IT-services in dynamic markets},
publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas
Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and
Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake,
Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth,
Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga
Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration
and Evaluation.” In On-The-Fly Computing – Individualized IT-Services in Dynamic
Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco
Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe
Des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn, 2023.
https://doi.org/10.5281/zenodo.8068466.
ieee: J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly
Computing – Individualized IT-services in dynamic markets, vol. 412, C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds.
Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.
mla: Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly
Computing – Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp.
85–104, doi:10.5281/zenodo.8068466.
short: 'J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A.
Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H.
Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing – Individualized IT-Services
in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.'
date_created: 2024-06-04T15:55:56Z
date_updated: 2024-06-04T15:56:45Z
department:
- _id: '574'
doi: 10.5281/zenodo.8068466
editor:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
intvolume: ' 412'
keyword:
- dice ngonga sfb901 sherif
language:
- iso: eng
page: 85–104
publication: On-The-Fly Computing – Individualized IT-services in dynamic markets
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: Configuration and Evaluation
type: book_chapter
user_id: '67199'
volume: 412
year: '2023'
...
---
_id: '54909'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Johannes
full_name: Fürnkranz, Johannes
last_name: Fürnkranz
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Hanselle JM, Fürnkranz J, Hüllermeier E. Probabilistic Scoring Lists for Interpretable
Machine Learning. In: Discovery Science. Springer Nature Switzerland; 2023.
doi:10.1007/978-3-031-45275-8_13'
apa: Hanselle, J. M., Fürnkranz, J., & Hüllermeier, E. (2023). Probabilistic
Scoring Lists for Interpretable Machine Learning. In Discovery Science.
Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-45275-8_13
bibtex: '@inbook{Hanselle_Fürnkranz_Hüllermeier_2023, place={Cham}, title={Probabilistic
Scoring Lists for Interpretable Machine Learning}, DOI={10.1007/978-3-031-45275-8_13},
booktitle={Discovery Science}, publisher={Springer Nature Switzerland}, author={Hanselle,
Jonas Manuel and Fürnkranz, Johannes and Hüllermeier, Eyke}, year={2023} }'
chicago: 'Hanselle, Jonas Manuel, Johannes Fürnkranz, and Eyke Hüllermeier. “Probabilistic
Scoring Lists for Interpretable Machine Learning.” In Discovery Science.
Cham: Springer Nature Switzerland, 2023. https://doi.org/10.1007/978-3-031-45275-8_13.'
ieee: 'J. M. Hanselle, J. Fürnkranz, and E. Hüllermeier, “Probabilistic Scoring
Lists for Interpretable Machine Learning,” in Discovery Science, Cham:
Springer Nature Switzerland, 2023.'
mla: Hanselle, Jonas Manuel, et al. “Probabilistic Scoring Lists for Interpretable
Machine Learning.” Discovery Science, Springer Nature Switzerland, 2023,
doi:10.1007/978-3-031-45275-8_13.
short: 'J.M. Hanselle, J. Fürnkranz, E. Hüllermeier, in: Discovery Science, Springer
Nature Switzerland, Cham, 2023.'
date_created: 2024-06-26T14:24:29Z
date_updated: 2024-06-26T14:25:50Z
department:
- _id: '660'
doi: 10.1007/978-3-031-45275-8_13
language:
- iso: eng
place: Cham
project:
- _id: '125'
name: 'TRR 318 - C2: TRR 318 - Subproject C2'
publication: Discovery Science
publication_identifier:
isbn:
- '9783031452741'
- '9783031452758'
issn:
- 0302-9743
- 1611-3349
publication_status: published
publisher: Springer Nature Switzerland
status: public
title: Probabilistic Scoring Lists for Interpretable Machine Learning
type: book_chapter
user_id: '72497'
year: '2023'
...
---
_id: '44512'
abstract:
- lang: eng
text: "For open world applications, deep neural networks (DNNs) need to be aware
of\r\npreviously unseen data and adaptable to evolving environments. Furthermore,
it\r\nis desirable to detect and learn novel classes which are not included in
the\r\nDNNs underlying set of semantic classes in an unsupervised fashion. The
method\r\nproposed in this article builds upon anomaly detection to retrieve\r\nout-of-distribution
(OoD) data as candidates for new classes. We thereafter\r\nextend the DNN by $k$
empty classes and fine-tune it on the OoD data samples.\r\nTo this end, we introduce
two loss functions, which 1) entice the DNN to assign\r\nOoD samples to the empty
classes and 2) to minimize the inner-class feature\r\ndistances between them.
Thus, instead of ground truth which contains labels for\r\nthe different novel
classes, the DNN obtains a single OoD label together with a\r\ndistance matrix,
which is computed in advance. We perform several experiments\r\nfor image classification
and semantic segmentation, which demonstrate that a\r\nDNN can extend its own
semantic space by multiple classes without having access\r\nto ground truth."
author:
- first_name: Svenja
full_name: Uhlemeyer, Svenja
last_name: Uhlemeyer
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Hanno
full_name: Gottschalk, Hanno
last_name: Gottschalk
citation:
ama: Uhlemeyer S, Lienen J, Hüllermeier E, Gottschalk H. Detecting Novelties with
Empty Classes. arXiv:230500983. Published online 2023.
apa: Uhlemeyer, S., Lienen, J., Hüllermeier, E., & Gottschalk, H. (2023). Detecting
Novelties with Empty Classes. In arXiv:2305.00983.
bibtex: '@article{Uhlemeyer_Lienen_Hüllermeier_Gottschalk_2023, title={Detecting
Novelties with Empty Classes}, journal={arXiv:2305.00983}, author={Uhlemeyer,
Svenja and Lienen, Julian and Hüllermeier, Eyke and Gottschalk, Hanno}, year={2023}
}'
chicago: Uhlemeyer, Svenja, Julian Lienen, Eyke Hüllermeier, and Hanno Gottschalk.
“Detecting Novelties with Empty Classes.” ArXiv:2305.00983, 2023.
ieee: S. Uhlemeyer, J. Lienen, E. Hüllermeier, and H. Gottschalk, “Detecting Novelties
with Empty Classes,” arXiv:2305.00983. 2023.
mla: Uhlemeyer, Svenja, et al. “Detecting Novelties with Empty Classes.” ArXiv:2305.00983,
2023.
short: S. Uhlemeyer, J. Lienen, E. Hüllermeier, H. Gottschalk, ArXiv:2305.00983
(2023).
date_created: 2023-05-05T11:37:00Z
date_updated: 2023-05-05T11:39:10Z
external_id:
arxiv:
- '2305.00983'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/pdf/2305.00983.pdf
oa: '1'
publication: arXiv:2305.00983
status: public
title: Detecting Novelties with Empty Classes
type: preprint
user_id: '44040'
year: '2023'
...
---
_id: '31880'
abstract:
- lang: eng
text: The notion of neural collapse refers to several emergent phenomena that have
been empirically observed across various canonical classification problems. During
the terminal phase of training a deep neural network, the feature embedding of
all examples of the same class tend to collapse to a single representation, and
the features of different classes tend to separate as much as possible. Neural
collapse is often studied through a simplified model, called the unconstrained
feature representation, in which the model is assumed to have "infinite expressivity"
and can map each data point to any arbitrary representation. In this work, we
propose a more realistic variant of the unconstrained feature representation that
takes the limited expressivity of the network into account. Empirical evidence
suggests that the memorization of noisy data points leads to a degradation (dilation)
of the neural collapse. Using a model of the memorization-dilation (M-D) phenomenon,
we show one mechanism by which different losses lead to different performances
of the trained network on noisy data. Our proofs reveal why label smoothing, a
modification of cross-entropy empirically observed to produce a regularization
effect, leads to improved generalization in classification tasks.
author:
- first_name: Duc Anh
full_name: Nguyen, Duc Anh
last_name: Nguyen
- first_name: Ron
full_name: Levie, Ron
last_name: Levie
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Gitta
full_name: Kutyniok, Gitta
last_name: Kutyniok
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: 'Nguyen DA, Levie R, Lienen J, Kutyniok G, Hüllermeier E. Memorization-Dilation:
Modeling Neural Collapse Under Noise. In: International Conference on Learning
Representations, ICLR. ; 2023.'
apa: 'Nguyen, D. A., Levie, R., Lienen, J., Kutyniok, G., & Hüllermeier, E.
(2023). Memorization-Dilation: Modeling Neural Collapse Under Noise. International
Conference on Learning Representations, ICLR. International Conference on
Learning Representations, ICLR, Kigali, Ruanda.'
bibtex: '@inproceedings{Nguyen_Levie_Lienen_Kutyniok_Hüllermeier_2023, title={Memorization-Dilation:
Modeling Neural Collapse Under Noise}, booktitle={International Conference on
Learning Representations, ICLR}, author={Nguyen, Duc Anh and Levie, Ron and Lienen,
Julian and Kutyniok, Gitta and Hüllermeier, Eyke}, year={2023} }'
chicago: 'Nguyen, Duc Anh, Ron Levie, Julian Lienen, Gitta Kutyniok, and Eyke Hüllermeier.
“Memorization-Dilation: Modeling Neural Collapse Under Noise.” In International
Conference on Learning Representations, ICLR, 2023.'
ieee: 'D. A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, and E. Hüllermeier, “Memorization-Dilation:
Modeling Neural Collapse Under Noise,” presented at the International Conference
on Learning Representations, ICLR, Kigali, Ruanda, 2023.'
mla: 'Nguyen, Duc Anh, et al. “Memorization-Dilation: Modeling Neural Collapse Under
Noise.” International Conference on Learning Representations, ICLR, 2023.'
short: 'D.A. Nguyen, R. Levie, J. Lienen, G. Kutyniok, E. Hüllermeier, in: International
Conference on Learning Representations, ICLR, 2023.'
conference:
location: Kigali, Ruanda
name: International Conference on Learning Representations, ICLR
date_created: 2022-06-14T14:48:36Z
date_updated: 2023-06-29T09:14:26Z
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2206.05530
oa: '1'
publication: International Conference on Learning Representations, ICLR
status: public
title: 'Memorization-Dilation: Modeling Neural Collapse Under Noise'
type: conference
user_id: '44040'
year: '2023'
...
---
_id: '45884'
author:
- first_name: Jonas Manuel
full_name: Hanselle, Jonas Manuel
id: '43980'
last_name: Hanselle
orcid: 0000-0002-1231-4985
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Felix
full_name: Mohr, Felix
last_name: Mohr
- first_name: Axel-Cyrille
full_name: Ngonga Ngomo, Axel-Cyrille
id: '65716'
last_name: Ngonga Ngomo
- first_name: Mohamed
full_name: Sherif, Mohamed
id: '67234'
last_name: Sherif
orcid: https://orcid.org/0000-0002-9927-2203
- first_name: Alexander
full_name: Tornede, Alexander
id: '38209'
last_name: Tornede
- first_name: Marcel Dominik
full_name: Wever, Marcel Dominik
id: '33176'
last_name: Wever
orcid: ' https://orcid.org/0000-0001-9782-6818'
citation:
ama: 'Hanselle JM, Hüllermeier E, Mohr F, et al. Configuration and Evaluation. In:
Haake C-J, Meyer auf der Heide F, Platzner M, Wachsmuth H, Wehrheim H, eds. On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets. Vol 412. Verlagsschriftenreihe
des Heinz Nixdorf Instituts. Heinz Nixdorf Institut, Universität Paderborn; 2023:85-104.
doi:10.5281/zenodo.8068466'
apa: Hanselle, J. M., Hüllermeier, E., Mohr, F., Ngonga Ngomo, A.-C., Sherif, M.,
Tornede, A., & Wever, M. D. (2023). Configuration and Evaluation. In C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.),
On-The-Fly Computing -- Individualized IT-services in dynamic markets (Vol.
412, pp. 85–104). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068466
bibtex: '@inbook{Hanselle_Hüllermeier_Mohr_Ngonga Ngomo_Sherif_Tornede_Wever_2023,
place={Paderborn}, series={Verlagsschriftenreihe des Heinz Nixdorf Instituts},
title={Configuration and Evaluation}, volume={412}, DOI={10.5281/zenodo.8068466},
booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets},
publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Hanselle, Jonas
Manuel and Hüllermeier, Eyke and Mohr, Felix and Ngonga Ngomo, Axel-Cyrille and
Sherif, Mohamed and Tornede, Alexander and Wever, Marcel Dominik}, editor={Haake,
Claus-Jochen and Meyer auf der Heide, Friedhelm and Platzner, Marco and Wachsmuth,
Henning and Wehrheim, Heike}, year={2023}, pages={85–104}, collection={Verlagsschriftenreihe
des Heinz Nixdorf Instituts} }'
chicago: 'Hanselle, Jonas Manuel, Eyke Hüllermeier, Felix Mohr, Axel-Cyrille Ngonga
Ngomo, Mohamed Sherif, Alexander Tornede, and Marcel Dominik Wever. “Configuration
and Evaluation.” In On-The-Fly Computing -- Individualized IT-Services in Dynamic
Markets, edited by Claus-Jochen Haake, Friedhelm Meyer auf der Heide, Marco
Platzner, Henning Wachsmuth, and Heike Wehrheim, 412:85–104. Verlagsschriftenreihe
Des Heinz Nixdorf Instituts. Paderborn: Heinz Nixdorf Institut, Universität Paderborn,
2023. https://doi.org/10.5281/zenodo.8068466.'
ieee: 'J. M. Hanselle et al., “Configuration and Evaluation,” in On-The-Fly
Computing -- Individualized IT-services in dynamic markets, vol. 412, C.-J.
Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds.
Paderborn: Heinz Nixdorf Institut, Universität Paderborn, 2023, pp. 85–104.'
mla: Hanselle, Jonas Manuel, et al. “Configuration and Evaluation.” On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp.
85–104, doi:10.5281/zenodo.8068466.
short: 'J.M. Hanselle, E. Hüllermeier, F. Mohr, A.-C. Ngonga Ngomo, M. Sherif, A.
Tornede, M.D. Wever, in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H.
Wachsmuth, H. Wehrheim (Eds.), On-The-Fly Computing -- Individualized IT-Services
in Dynamic Markets, Heinz Nixdorf Institut, Universität Paderborn, Paderborn,
2023, pp. 85–104.'
date_created: 2023-07-07T07:50:53Z
date_updated: 2023-07-07T11:20:12Z
ddc:
- '040'
department:
- _id: '7'
doi: 10.5281/zenodo.8068466
editor:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
file:
- access_level: open_access
content_type: application/pdf
creator: florida
date_created: 2023-07-07T07:50:34Z
date_updated: 2023-07-07T11:20:11Z
file_id: '45885'
file_name: B2-Chapter-SFB-Buch-Final.pdf
file_size: 895091
relation: main_file
file_date_updated: 2023-07-07T11:20:11Z
has_accepted_license: '1'
intvolume: ' 412'
language:
- iso: eng
oa: '1'
page: 85-104
place: Paderborn
project:
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '10'
grant_number: '160364472'
name: 'SFB 901 - B2: Konfiguration und Bewertung (B02)'
publication: On-The-Fly Computing -- Individualized IT-services in dynamic markets
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: Configuration and Evaluation
type: book_chapter
user_id: '477'
volume: 412
year: '2023'
...
---
_id: '45886'
author:
- first_name: Heike
full_name: Wehrheim, Heike
id: '573'
last_name: Wehrheim
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
- first_name: Steffen
full_name: Becker, Steffen
last_name: Becker
- first_name: Matthias
full_name: Becker, Matthias
last_name: Becker
- first_name: Cedric
full_name: Richter, Cedric
id: '50003'
last_name: Richter
- first_name: Arnab
full_name: Sharma, Arnab
id: '67200'
last_name: Sharma
citation:
ama: 'Wehrheim H, Hüllermeier E, Becker S, Becker M, Richter C, Sharma A. Composition
Analysis in Unknown Contexts. In: Haake C-J, Meyer auf der Heide F, Platzner M,
Wachsmuth H, Wehrheim H, eds. On-The-Fly Computing -- Individualized IT-Services
in Dynamic Markets. Vol 412. Verlagsschriftenreihe des Heinz Nixdorf Instituts.
Heinz Nixdorf Institut, Universität Paderborn; 2023:105-123. doi:10.5281/zenodo.8068510'
apa: Wehrheim, H., Hüllermeier, E., Becker, S., Becker, M., Richter, C., & Sharma,
A. (2023). Composition Analysis in Unknown Contexts. In C.-J. Haake, F. Meyer
auf der Heide, M. Platzner, H. Wachsmuth, & H. Wehrheim (Eds.), On-The-Fly
Computing -- Individualized IT-services in dynamic markets (Vol. 412, pp.
105–123). Heinz Nixdorf Institut, Universität Paderborn. https://doi.org/10.5281/zenodo.8068510
bibtex: '@inbook{Wehrheim_Hüllermeier_Becker_Becker_Richter_Sharma_2023, place={Paderborn},
series={Verlagsschriftenreihe des Heinz Nixdorf Instituts}, title={Composition
Analysis in Unknown Contexts}, volume={412}, DOI={10.5281/zenodo.8068510},
booktitle={On-The-Fly Computing -- Individualized IT-services in dynamic markets},
publisher={Heinz Nixdorf Institut, Universität Paderborn}, author={Wehrheim, Heike
and Hüllermeier, Eyke and Becker, Steffen and Becker, Matthias and Richter, Cedric
and Sharma, Arnab}, editor={Haake, Claus-Jochen and Meyer auf der Heide, Friedhelm
and Platzner, Marco and Wachsmuth, Henning and Wehrheim, Heike}, year={2023},
pages={105–123}, collection={Verlagsschriftenreihe des Heinz Nixdorf Instituts}
}'
chicago: 'Wehrheim, Heike, Eyke Hüllermeier, Steffen Becker, Matthias Becker, Cedric
Richter, and Arnab Sharma. “Composition Analysis in Unknown Contexts.” In On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake, Friedhelm Meyer auf der Heide, Marco Platzner, Henning Wachsmuth, and Heike
Wehrheim, 412:105–23. Verlagsschriftenreihe Des Heinz Nixdorf Instituts. Paderborn:
Heinz Nixdorf Institut, Universität Paderborn, 2023. https://doi.org/10.5281/zenodo.8068510.'
ieee: 'H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, and A. Sharma,
“Composition Analysis in Unknown Contexts,” in On-The-Fly Computing -- Individualized
IT-services in dynamic markets, vol. 412, C.-J. Haake, F. Meyer auf der Heide,
M. Platzner, H. Wachsmuth, and H. Wehrheim, Eds. Paderborn: Heinz Nixdorf Institut,
Universität Paderborn, 2023, pp. 105–123.'
mla: Wehrheim, Heike, et al. “Composition Analysis in Unknown Contexts.” On-The-Fly
Computing -- Individualized IT-Services in Dynamic Markets, edited by Claus-Jochen
Haake et al., vol. 412, Heinz Nixdorf Institut, Universität Paderborn, 2023, pp.
105–23, doi:10.5281/zenodo.8068510.
short: 'H. Wehrheim, E. Hüllermeier, S. Becker, M. Becker, C. Richter, A. Sharma,
in: C.-J. Haake, F. Meyer auf der Heide, M. Platzner, H. Wachsmuth, H. Wehrheim
(Eds.), On-The-Fly Computing -- Individualized IT-Services in Dynamic Markets,
Heinz Nixdorf Institut, Universität Paderborn, Paderborn, 2023, pp. 105–123.'
date_created: 2023-07-07T07:56:08Z
date_updated: 2023-07-07T11:19:40Z
ddc:
- '004'
department:
- _id: '7'
doi: 10.5281/zenodo.8068510
editor:
- first_name: Claus-Jochen
full_name: Haake, Claus-Jochen
last_name: Haake
- first_name: Friedhelm
full_name: Meyer auf der Heide, Friedhelm
last_name: Meyer auf der Heide
- first_name: Marco
full_name: Platzner, Marco
last_name: Platzner
- first_name: Henning
full_name: Wachsmuth, Henning
last_name: Wachsmuth
- first_name: Heike
full_name: Wehrheim, Heike
last_name: Wehrheim
file:
- access_level: open_access
content_type: application/pdf
creator: florida
date_created: 2023-07-07T07:55:57Z
date_updated: 2023-07-07T11:19:40Z
file_id: '45887'
file_name: B3-Chapter-SFB-Buch-Final.pdf
file_size: 370888
relation: main_file
file_date_updated: 2023-07-07T11:19:40Z
has_accepted_license: '1'
intvolume: ' 412'
language:
- iso: eng
oa: '1'
page: 105-123
place: Paderborn
project:
- _id: '1'
grant_number: '160364472'
name: 'SFB 901: SFB 901: On-The-Fly Computing - Individualisierte IT-Dienstleistungen
in dynamischen Märkten '
- _id: '3'
name: 'SFB 901 - B: SFB 901 - Project Area B'
- _id: '11'
name: 'SFB 901 - B3: SFB 901 - Subproject B3'
publication: On-The-Fly Computing -- Individualized IT-services in dynamic markets
publisher: Heinz Nixdorf Institut, Universität Paderborn
series_title: Verlagsschriftenreihe des Heinz Nixdorf Instituts
status: public
title: Composition Analysis in Unknown Contexts
type: book_chapter
user_id: '477'
volume: 412
year: '2023'
...
---
_id: '45911'
abstract:
- lang: eng
text: "Label noise poses an important challenge in machine learning, especially
in\r\ndeep learning, in which large models with high expressive power dominate
the\r\nfield. Models of that kind are prone to memorizing incorrect labels, thereby\r\nharming
generalization performance. Many methods have been proposed to address\r\nthis
problem, including robust loss functions and more complex label correction\r\napproaches.
Robust loss functions are appealing due to their simplicity, but\r\ntypically
lack flexibility, while label correction usually adds substantial\r\ncomplexity
to the training setup. In this paper, we suggest to address the\r\nshortcomings
of both methodologies by \"ambiguating\" the target information,\r\nadding additional,
complementary candidate labels in case the learner is not\r\nsufficiently convinced
of the observed training label. More precisely, we\r\nleverage the framework of
so-called superset learning to construct set-valued\r\ntargets based on a confidence
threshold, which deliver imprecise yet more\r\nreliable beliefs about the ground-truth,
effectively helping the learner to\r\nsuppress the memorization effect. In an
extensive empirical evaluation, our\r\nmethod demonstrates favorable learning
behavior on synthetic and real-world\r\nnoise, confirming the effectiveness in
detecting and correcting erroneous\r\ntraining labels."
author:
- first_name: Julian
full_name: Lienen, Julian
id: '44040'
last_name: Lienen
- first_name: Eyke
full_name: Hüllermeier, Eyke
id: '48129'
last_name: Hüllermeier
citation:
ama: Lienen J, Hüllermeier E. Mitigating Label Noise through Data Ambiguation. arXiv:230513764.
Published online 2023.
apa: Lienen, J., & Hüllermeier, E. (2023). Mitigating Label Noise through Data
Ambiguation. In arXiv:2305.13764.
bibtex: '@article{Lienen_Hüllermeier_2023, title={Mitigating Label Noise through
Data Ambiguation}, journal={arXiv:2305.13764}, author={Lienen, Julian and Hüllermeier,
Eyke}, year={2023} }'
chicago: Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data
Ambiguation.” ArXiv:2305.13764, 2023.
ieee: J. Lienen and E. Hüllermeier, “Mitigating Label Noise through Data Ambiguation,”
arXiv:2305.13764. 2023.
mla: Lienen, Julian, and Eyke Hüllermeier. “Mitigating Label Noise through Data
Ambiguation.” ArXiv:2305.13764, 2023.
short: J. Lienen, E. Hüllermeier, ArXiv:2305.13764 (2023).
date_created: 2023-07-09T11:25:48Z
date_updated: 2023-07-09T11:26:21Z
external_id:
arxiv:
- '2305.13764'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2305.13764
oa: '1'
publication: arXiv:2305.13764
status: public
title: Mitigating Label Noise through Data Ambiguation
type: preprint
user_id: '44040'
year: '2023'
...