Viktor Bengs

Publication Activity (10 Years)

Years Active: 2019-2024
Publications (10 Years): 40

Top Topics

Multi Armed Bandits

Iterative Deepening

Top Venues

Publications

Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
Identifying Copeland Winners in Dueling Bandits with Indifferences. AISTATS (2024)
Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? CoRR (2024)
Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier
Approximating the Shapley Value without Marginal Contributions. AAAI (2024)
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
Approximating the Shapley Value without Marginal Contributions. CoRR (2023)
Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier
Identifying Copeland Winners in Dueling Bandits with Indifferences. CoRR (2023)
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
On the Calibration of Probabilistic Classifier Sets. AISTATS (2023)
Viktor Bengs, Eyke Hüllermeier
Multi-armed bandits with censored consumption of resources. Mach. Learn. 112 (1) (2023)
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)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. CoRR (2023)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification. ICML (2023)
Jasmin Brandt, Elias Schede, Shivam Sharma, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration. LWDA (2023)
Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier
A Survey of Reinforcement Learning from Human Feedback. CoRR (2023)
Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. AAAI (2023)
Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier
Iterative Deepening Hyperband. CoRR (2023)
Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier
Second-Order Uncertainty Quantification: A Distance-Based Approach. CoRR (2023)
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 (2022)
Viktor Bengs, Eyke Hüllermeier, Willem Waegeman
Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation. NeurIPS (2022)
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. CoRR (2022)
Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman
On Calibration of Ensemble-Based Credal Predictors. CoRR (2022)
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)
Elias Schede, Jasmin Brandt, Alexander Tornede, Marcel Wever, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
A Survey of Methods for Automated Algorithm Configuration. CoRR (2022)
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 (2022)
Alexander Tornede, Viktor Bengs, Eyke Hüllermeier
Machine Learning for Online Algorithm Selection under Censored Feedback. AAAI (2022)
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
Non-Stationary Dueling Bandits. CoRR (2022)
Jasmin Brandt, Elias Schede, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier, Kevin Tierney
AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration. CoRR (2022)
Jasmin Brandt, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier
Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget. CoRR (2022)
Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier
Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models. ICML (2022)
Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier
Identification of the Generalized Condorcet Winner in Multi-dueling Bandits. NeurIPS (2021)
Alexander Tornede, Viktor Bengs, Eyke Hüllermeier
Machine Learning for Online Algorithm Selection under Censored Feedback. CoRR (2021)
Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier
On testing transitivity in online preference learning. Mach. Learn. 110 (8) (2021)
Felix Mohr, Viktor Bengs, Eyke Hüllermeier
Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model. AAAI (2021)
Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier
Identifying Top-k Players in Cooperative Games via Shapley Bandits. LWDA (2021)
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 (2021)
Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier
Testification of Condorcet Winners in dueling bandits. UAI (2021)
Adil El Mesaoudi-Paul, Viktor Bengs, Eyke Hüllermeier
Online Preselection with Context Information under the Plackett-Luce Model. CoRR (2020)
Viktor Bengs, Eyke Hüllermeier
Preselection Bandits. ICML (2020)
Viktor Bengs, Eyke Hüllermeier
Multi-Armed Bandits with Censored Consumption of Resources. CoRR (2020)
Adil El Mesaoudi-Paul, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney
Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach. LION (2020)
Viktor Bengs, Eyke Hüllermeier
Preselection Bandits under the Plackett-Luce Model. CoRR (2019)
Viktor Bengs, Matthias Eulert, Hajo Holzmann
Asymptotic confidence sets for the jump curve in bivariate regression problems. J. Multivar. Anal. 173 (2019)