| Preference-based online learning with dueling bandits: A survey. V Bengs, R Busa-Fekete, A El Mesaoudi-Paul, E Hüllermeier J. Mach. Learn. Res. 22, 7:1-7:108, 2021 | 58* | 2021 |
| Preselection bandits V Bengs, E Hüllermeier International Conference on Machine Learning, 778-787, 2020 | 11* | 2020 |
| Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation V Bengs, E Hüllermeier, W Waegeman Advances in Neural Information Processing Systems, 2022 | 8* | 2022 |
| Pool-based realtime algorithm configuration: A preselection bandit approach A El Mesaoudi-Paul, D Weiß, V Bengs, E Hüllermeier, K Tierney Learning and Intelligent Optimization: 14th International Conference, LION …, 2020 | 8 | 2020 |
| A survey of methods for automated algorithm configuration E Schede, J Brandt, A Tornede, M Wever, V Bengs, E Hüllermeier, ... Journal of Artificial Intelligence Research 75, 425-487, 2022 | 7 | 2022 |
| Uniform approximation in classical weak convergence theory V Bengs, H Holzmann arXiv preprint arXiv:1903.09864, 2019 | 6 | 2019 |
| Online preselection with context information under the Plackett-Luce model AE Mesaoudi-Paul, V Bengs, E Hüllermeier arXiv preprint arXiv:2002.04275, 2020 | 3 | 2020 |
| Adaptive confidence sets for kink estimation V Bengs, H Holzmann | 3 | 2019 |
| Approximating the shapley value without marginal contributions P Kolpaczki, V Bengs, E Hüllermeier arXiv preprint arXiv:2302.00736, 2023 | 2 | 2023 |
| Multi-armed bandits with censored consumption of resources V Bengs, E Hüllermeier Machine Learning, 1-24, 2022 | 2 | 2022 |
| Non-Stationary Dueling Bandits P Kolpaczki, V Bengs, E Hüllermeier arXiv preprint arXiv:2202.00935, 2022 | 2 | 2022 |
| Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget J Brandt, V Bengs, B Haddenhorst, E Hüllermeier Advances in Neural Information Processing Systems, 2022 | 2 | 2022 |
| Identification of the generalized Condorcet winner in multi-dueling bandits B Haddenhorst, V Bengs, E Hüllermeier Advances in Neural Information Processing Systems 34, 25904-25916, 2021 | 2 | 2021 |
| Iterative Deepening Hyperband J Brandt, M Wever, D Iliadis, V Bengs, E Hüllermeier arXiv preprint arXiv:2302.00511, 2023 | 1 | 2023 |
| On Second-Order Scoring Rules for Epistemic Uncertainty Quantification V Bengs, E Hüllermeier, W Waegeman arXiv preprint arXiv:2301.12736, 2023 | 1 | 2023 |
| Machine learning for online algorithm selection under censored feedback A Tornede, V Bengs, E Hüllermeier Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 10370 …, 2022 | 1 | 2022 |
| Testification of Condorcet Winners in dueling bandits B Haddenhorst, V Bengs, J Brandt, E Hüllermeier Uncertainty in Artificial Intelligence, 1195-1205, 2021 | 1 | 2021 |
| On testing transitivity in online preference learning B Haddenhorst, V Bengs, E Hüllermeier Machine Learning 110, 2063-2084, 2021 | 1 | 2021 |
| Single player Monte-Carlo tree search based on the Plackett-Luce Model F Mohr, V Bengs, E Hüllermeier Proceedings of the AAAI Conference on Artificial Intelligence 35 (14), 12373 …, 2021 | 1 | 2021 |
| Asymptotic confidence sets for the jump curve in bivariate regression problems V Bengs, M Eulert, H Holzmann Journal of Multivariate Analysis 173, 291-312, 2019 | 1 | 2019 |
Eyke HüllermeierProfessor of Computer Science, Paderborn UniversityVerified email at upb.de
