Viktor Bengs

According to our database, Viktor Bengs authored at least 32 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?

Mira Jürgens, Nis Meinert, Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

CoRR, 2024

Approximating the Shapley Value without Marginal Contributions.

Patrick Kolpaczki, Viktor Bengs, Maximilian Muschalik, Eyke Hüllermeier

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Multi-armed bandits with censored consumption of resources.

Viktor Bengs, Eyke Hüllermeier

Mach. Learn., January, 2023

A Survey of Reinforcement Learning from Human Feedback.

Timo Kaufmann, Paul Weng, Viktor Bengs, Eyke Hüllermeier

CoRR, 2023

Second-Order Uncertainty Quantification: A Distance-Based Approach.

Yusuf Sale, Viktor Bengs, Michele Caprio, Eyke Hüllermeier

CoRR, 2023

Identifying Copeland Winners in Dueling Bandits with Indifferences.

Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier

CoRR, 2023

Approximating the Shapley Value without Marginal Contributions.

Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier

CoRR, 2023

Iterative Deepening Hyperband.

Jasmin Brandt, Marcel Wever, Dimitrios Iliadis, Viktor Bengs, Eyke Hüllermeier

CoRR, 2023

Contextual Preselection Methods in Pool-based Realtime Algorithm Configuration.

Jasmin Brandt, Elias Schede, Shivam Sharma, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

Proceedings of the Lernen, 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

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

On Second-Order Scoring Rules for Epistemic Uncertainty Quantification.

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Proceedings of the International Conference on Machine Learning, 2023

On the Calibration of Probabilistic Classifier Sets.

Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

AC-Band: A Combinatorial Bandit-Based Approach to Algorithm Configuration.

Jasmin Brandt, Elias Schede, Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

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

On Calibration of Ensemble-Based Credal Predictors.

Thomas Mortier, Viktor Bengs, Eyke Hüllermeier, Stijn Luca, Willem Waegeman

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

Non-Stationary Dueling Bandits.

Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier

CoRR, 2022

Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget.

Jasmin Brandt, Viktor Bengs, Björn Haddenhorst, Eyke Hüllermeier

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation.

Viktor Bengs, Eyke Hüllermeier, Willem Waegeman

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models.

Viktor Bengs, Aadirupa Saha, Eyke Hüllermeier

Proceedings of the International Conference on Machine Learning, 2022

Machine Learning for Online Algorithm Selection under Censored Feedback.

Alexander Tornede, Viktor Bengs, Eyke Hüllermeier

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

On testing transitivity in online preference learning.

Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier

Mach. Learn., 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

Testification of Condorcet Winners in dueling bandits.

Björn Haddenhorst, Viktor Bengs, Jasmin Brandt, Eyke Hüllermeier

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Identification of the Generalized Condorcet Winner in Multi-dueling Bandits.

Björn Haddenhorst, Viktor Bengs, Eyke Hüllermeier

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Identifying Top-k Players in Cooperative Games via Shapley Bandits.

Patrick Kolpaczki, Viktor Bengs, Eyke Hüllermeier

Proceedings of the LWDA 2021 Workshops: FGWM, 2021

Single Player Monte-Carlo Tree Search Based on the Plackett-Luce Model.

Felix Mohr, Viktor Bengs, Eyke Hüllermeier

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Online Preselection with Context Information under the Plackett-Luce Model.

Adil El Mesaoudi-Paul, Viktor Bengs, Eyke Hüllermeier

CoRR, 2020

Pool-Based Realtime Algorithm Configuration: A Preselection Bandit Approach.

Adil El Mesaoudi-Paul, Dimitri Weiß, Viktor Bengs, Eyke Hüllermeier, Kevin Tierney

Proceedings of the Learning and Intelligent Optimization - 14th International Conference, 2020

Preselection Bandits.

Viktor Bengs, Eyke Hüllermeier

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Asymptotic confidence sets for the jump curve in bivariate regression problems.

Viktor Bengs, Matthias Eulert, Hajo Holzmann

J. Multivar. Anal., 2019

Preselection Bandits under the Plackett-Luce Model.

Viktor Bengs, Eyke Hüllermeier

CoRR, 2019