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Viktor Bengs
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2020 – today
- 2024
- [c20]Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier:
Approximating the Shapley Value without Marginal Contributions. AAAI 2024: 13246-13255 - [c19]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. AISTATS 2024: 226-234 - [c18]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? ICML 2024 - [c17]Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier:
Second-Order Uncertainty Quantification: A Distance-Based Approach. ICML 2024 - [c16]Jasmin Brandt, Marcel Wever, Viktor Bengs, Eyke Hüllermeier:
Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO. IJCAI 2024: 3742-3750 - [i18]Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR abs/2402.09056 (2024) - 2023
- [j5]Viktor Bengs
, Eyke Hüllermeier:
Multi-armed bandits with censored consumption of resources. Mach. Learn. 112(1): 217-240 (2023) - [c15]Jasmin Brandt, Elias Schede, Björn Haddenhorst
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI 2023: 12355-12363 - [c14]Thomas Mortier
On the Calibration of Probabilistic Classifier Sets. AISTATS 2023: 8857-8870 - [c13]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML 2023: 2078-2091 - [c12]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration (Extended Abstract). IJCAI 2023: 6964-6968 - [c11]Jasmin Brandt, Elias Schede, Shivam Sharma, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. LWDA 2023: 492-505 - [i17]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR abs/2301.12736 (2023) - [i16]Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier:
Iterative Deepening Hyperband. CoRR abs/2302.00511 (2023) - [i15]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Approximating the Shapley Value without Marginal Contributions. CoRR abs/2302.00736 (2023) - [i14]Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Identifying Copeland Winners in Dueling Bandits with Indifferences. CoRR abs/2310.00750 (2023) - [i13]Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier:
Second-Order Uncertainty Quantification: A Distance-Based Approach. CoRR abs/2312.00995 (2023) - [i12]Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier:
A Survey of Reinforcement Learning from Human Feedback. CoRR abs/2312.14925 (2023) - 2022
- [j4]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration. J. Artif. Intell. Res. 75: 425-487 (2022) - [c10]Alexander Tornede, Viktor Bengs, Eyke Hüllermeier:
Machine Learning for Online Algorithm Selection under Censored Feedback. AAAI 2022: 10370-10380 - [c9]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. ICML 2022: 1764-1786 - [c8]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS 2022 - [c7]Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier:
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. NeurIPS 2022 - [i11]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Non-Stationary Dueling Bandits. CoRR abs/2202.00935 (2022) - [i10]Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney:
A Survey of Methods for Automated Algorithm Configuration. CoRR abs/2202.01651 (2022) - [i9]Jasmin Brandt, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. CoRR abs/2202.04487 (2022) - [i8]Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier:
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. CoRR abs/2202.04593 (2022) - [i7]Viktor Bengs, Eyke Hüllermeier, Willem Waegeman:
On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. CoRR abs/2203.06102 (2022) - [i6]Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman:
On Calibration of Ensemble-Based Credal Predictors. CoRR abs/2205.10082 (2022) - [i5]Jasmin Brandt, Elias Schede, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier, Kevin Tierney:
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. CoRR abs/2212.00333 (2022) - 2021
- [j3]Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier:
Preference-based Online Learning with Dueling Bandits: A Survey. J. Mach. Learn. Res. 22: 7:1-7:108 (2021) - [j2]Björn Haddenhorst
On testing transitivity in online preference learning. Mach. Learn. 110(8): 2063-2084 (2021) - [c6]Felix Mohr, Viktor Bengs, Eyke Hüllermeier:
Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI 2021: 12373-12381 - [c5]Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier:
Identifying Top-k Players in Cooperative Games via Shapley Bandits. LWDA 2021: 133-144 - [c4]Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier:
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. NeurIPS 2021: 25904-25916 - [c3]Björn Haddenhorst
Testification of Condorcet Winners in dueling bandits. UAI 2021: 1195-1205 - [i4]Alexander Tornede, Viktor Bengs, Eyke Hüllermeier:
Machine Learning for Online Algorithm Selection under Censored Feedback. CoRR abs/2109.06234 (2021) - 2020
- [c2]Viktor Bengs, Eyke Hüllermeier:
Preselection Bandits. ICML 2020: 778-787 - [c1]Adil El Mesaoudi-Paul, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. LION 2020: 216-232 - [i3]Adil El Mesaoudi-Paul, Viktor Bengs, Eyke Hüllermeier:
Online Preselection with Context Information under the Plackett-Luce Model. CoRR abs/2002.04275 (2020) - [i2]Viktor Bengs, Eyke Hüllermeier:
Multi-Armed Bandits with Censored Consumption of Resources. CoRR abs/2011.00813 (2020)
2010 – 2019
- 2019
- [j1]Viktor Bengs, Matthias Eulert, Hajo Holzmann:
Asymptotic confidence sets for the jump curve in bivariate regression problems. J. Multivar. Anal. 173: 291-312 (2019) - [i1]Viktor Bengs, Eyke Hüllermeier:
Preselection Bandits under the Plackett-Luce Model. CoRR abs/1907.06123 (2019)
Coauthor Index
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