Bengs, Viktor

26 publications

MLJ 2026 MORE-PLR: Multi-Output Regression Employed for Partial Label Ranking Santo M. A. R. Thies, Juan C. Alfaro, Viktor Bengs

MLJ 2025 A Calibration Test for Evaluating Set-Based Epistemic Uncertainty Representations Mira Jürgens, Thomas Mortier, Eyke Hüllermeier, Viktor Bengs, Willem Waegeman

TMLR 2025 A Survey of Reinforcement Learning from Human Feedback Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier

AAAI 2024 Approximating the Shapley Value Without Marginal Contributions Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier

IJCAI 2024 Best Arm Identification with Retroactively Increased Sampling Budget for More Resource-Efficient HPO Jasmin Brandt, Marcel Wever, Viktor Bengs, Eyke Hüllermeier

AISTATS 2024 Identifying Copeland Winners in Dueling Bandits with Indifferences Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier

ICML 2024 Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? Mira Juergens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

TMLR 2024 Piecewise-Stationary Dueling Bandits Patrick Kolpaczki, Eyke Hüllermeier, Viktor Bengs

ICMLW 2024 Relatively Rational: Learning Utilities and Rationalities Jointly from Pairwise Preferences Taku Yamagata, Tobias Oberkofler, Timo Kaufmann, Viktor Bengs, Eyke Hüllermeier, Raul Santos-Rodriguez

ICML 2024 Second-Order Uncertainty Quantification: A Distance-Based Approach Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier

IJCAI 2023 A Survey of Methods for Automated Algorithm Configuration (Extended Abstract) Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

AAAI 2023 AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

MLJ 2023 Multi-Armed Bandits with Censored Consumption of Resources Viktor Bengs, Eyke Hüllermeier

ICML 2023 On Second-Order Scoring Rules for Epistemic Uncertainty Quantification Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

AISTATS 2023 On the Calibration of Probabilistic Classifier Sets Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

JAIR 2022 A Survey of Methods for Automated Algorithm Configuration Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

NeurIPS 2022 Finding Optimal Arms in Non-Stochastic Combinatorial Bandits with Semi-Bandit Feedback and Finite Budget Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier

AAAI 2022 Machine Learning for Online Algorithm Selection Under Censored Feedback Alexander Tornede, Viktor Bengs, Eyke Hüllermeier

NeurIPS 2022 Pitfalls of Epistemic Uncertainty Quantification Through Loss Minimisation Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

ICML 2022 Stochastic Contextual Dueling Bandits Under Linear Stochastic Transitivity Models Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier

NeurIPS 2021 Identification of the Generalized Condorcet Winner in Multi-Dueling Bandits Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier

MLJ 2021 On Testing Transitivity in Online Preference Learning Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier

JMLR 2021 Preference-Based Online Learning with Dueling Bandits: A Survey Viktor Bengs, Róbert Busa-Fekete, Adil El Mesaoudi-Paul, Eyke Hüllermeier

AAAI 2021 Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model Felix Mohr, Viktor Bengs, Eyke Hüllermeier

UAI 2021 Testification of Condorcet Winners in Dueling Bandits Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier

ICML 2020 Preselection Bandits Viktor Bengs, Eyke Hüllermeier