Preselection Bandits under the Plackett-Luce Model. - Viktor Bengs; Eyke Hüllermeier (CoRR, 2019)

Asymptotic confidence sets for the jump curve in bivariate regression problems. - Viktor Bengs; Matthias Eulert; Hajo Holzmann (J. Multivar. Anal., 2019)

Preselection Bandits. - Viktor Bengs; Eyke Hüllermeier (ICML, 2020)

Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. - Adil El Mesaoudi-Paul; Dimitri Weiß; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (LION, 2020)

Online Preselection with Context Information under the Plackett-Luce Model. - Adil El Mesaoudi-Paul; Viktor Bengs; Eyke Hüllermeier (CoRR, 2020)

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

Identifying Top-k Players in Cooperative Games via Shapley Bandits. - Patrick Kolpaczki; Viktor Bengs; Eyke Hüllermeier (LWDA, 2021)

Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. - Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier (NeurIPS, 2021)

Testification of Condorcet Winners in dueling bandits. - Björn Haddenhorst; Viktor Bengs; Jasmin Brandt; Eyke Hüllermeier (UAI, 2021)

Preference-based Online Learning with Dueling Bandits: A Survey. - Viktor Bengs; Róbert Busa-Fekete; Adil El Mesaoudi-Paul; Eyke Hüllermeier (J. Mach. Learn. Res., 2021)

On testing transitivity in online preference learning. - Björn Haddenhorst; Viktor Bengs; Eyke Hüllermeier (Mach. Learn., 2021)

Machine Learning for Online Algorithm Selection under Censored Feedback. - Alexander Tornede; Viktor Bengs; Eyke Hüllermeier (AAAI, 2022)

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. - Viktor Bengs; Aadirupa Saha; Eyke Hüllermeier (ICML, 2022)

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. - Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (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 (NeurIPS, 2022)

Non-Stationary Dueling Bandits. - Patrick Kolpaczki; Viktor Bengs; Eyke Hüllermeier (CoRR, 2022)

On the Difficulty of Epistemic Uncertainty Quantification in Machine Learning: The Case of Direct Uncertainty Estimation through Loss Minimisation. - Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (CoRR, 2022)

On Calibration of Ensemble-Based Credal Predictors. - Thomas Mortier; Viktor Bengs; Eyke Hüllermeier; Stijn Luca; Willem Waegeman (CoRR, 2022)

A Survey of Methods for Automated Algorithm Configuration. - Elias Schede; Jasmin Brandt; Alexander Tornede; Marcel Wever; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (J. Artif. Intell. Res., 2022)

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

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

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. - Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (ICML, 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 (IJCAI, 2023)

Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. - Jasmin Brandt; Elias Schede; Shivam Sharma; Viktor Bengs; Eyke Hüllermeier; Kevin Tierney (LWDA, 2023)

Shapley-Based Feature Selection for Online Algorithm Selection. - Patrick Becker; Viktor Bengs (PKDD/ECML Workshops (1), 2023)

Iterative Deepening Hyperband. - Jasmin Brandt; Marcel Wever; Dimitrios Iliadis; Viktor Bengs; Eyke Hüllermeier (CoRR, 2023)

Approximating the Shapley Value without Marginal Contributions. - Patrick Kolpaczki; Viktor Bengs; Eyke Hüllermeier (CoRR, 2023)

Multi-armed bandits with censored consumption of resources. - Viktor Bengs; Eyke Hüllermeier (Mach. Learn., 2023)

Approximating the Shapley Value without Marginal Contributions. - Patrick Kolpaczki; Viktor Bengs; Maximilian Muschalik; Eyke Hüllermeier (AAAI, 2024)

Identifying Copeland Winners in Dueling Bandits with Indifferences. - Viktor Bengs; Björn Haddenhorst; Eyke Hüllermeier (AISTATS, 2024)

MORE-PLR: Multi-Output Regression Employed for Partial Label Ranking. - Santo M. A. R. Thies; Juan C. Alfaro; Viktor Bengs (DS (1), 2024)

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? - Mira Jürgens; Nis Meinert; Viktor Bengs; Eyke Hüllermeier; Willem Waegeman (ICML, 2024)

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

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

Piecewise-Stationary Dueling Bandits. - Patrick Kolpaczki; Eyke Hüllermeier; Viktor Bengs (Trans. Mach. Learn. Res., 2024)

A Survey of Reinforcement Learning from Human Feedback. - Timo Kaufmann; Paul Weng; Viktor Bengs; Eyke Hüllermeier (Trans. Mach. Learn. Res., 2025)

A comparative analysis of rank aggregation methods for the partial label ranking problem. - Jiayi Wang; Juan C. Alfaro; Viktor Bengs (CoRR, 2025)

A calibration test for evaluating set-based epistemic uncertainty representations. - Mira Jürgens; Thomas Mortier; Eyke Hüllermeier; Viktor Bengs; Willem Waegeman (Mach. Learn., 2025)