| 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 | 103 | 2021 |
| 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 | 47 | 2022 |
| A survey of reinforcement learning from human feedback T Kaufmann, P Weng, V Bengs, E Hüllermeier arXiv preprint arXiv:2312.14925, 2023 | 43 | 2023 |
| Pitfalls of epistemic uncertainty quantification through loss minimisation V Bengs, E Hüllermeier, W Waegeman Advances in Neural Information Processing Systems 35, 29205-29216, 2022 | 38* | 2022 |
| Stochastic contextual dueling bandits under linear stochastic transitivity models V Bengs, A Saha, E Hüllermeier International Conference on Machine Learning, 1764-1786, 2022 | 20 | 2022 |
| On second-order scoring rules for epistemic uncertainty quantification V Bengs, E Hüllermeier, W Waegeman International Conference on Machine Learning, 2078-2091, 2023 | 15 | 2023 |
| Preselection bandits V Bengs, E Hüllermeier International Conference on Machine Learning, 778-787, 2020 | 15* | 2020 |
| 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 | 14 | 2020 |
| Approximating the shapley value without marginal contributions P Kolpaczki, V Bengs, M Muschalik, E Hüllermeier Proceedings of the AAAI Conference on Artificial Intelligence 38 (12), 13246 …, 2024 | 12 | 2024 |
| Second-order uncertainty quantification: A distance-based approach Y Sale, V Bengs, M Caprio, E Hüllermeier Proceedings of machine Learning Research, ICML 2024, 2024 | 12 | 2024 |
| Uniform approximation in classical weak convergence theory V Bengs, H Holzmann arXiv preprint arXiv:1903.09864, 2019 | 10 | 2019 |
| 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 | 9 | 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 | 8 | 2021 |
| Non-stationary dueling bandits P Kolpaczki, V Bengs, E Hüllermeier arXiv preprint arXiv:2202.00935, 2022 | 6 | 2022 |
| Ac-band: A combinatorial bandit-based approach to algorithm configuration J Brandt, E Schede, B Haddenhorst, V Bengs, E Hüllermeier, K Tierney Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 12355 …, 2023 | 4 | 2023 |
| Multi-armed bandits with censored consumption of resources V Bengs, E Hüllermeier Machine Learning, 1-24, 2022 | 4 | 2022 |
| 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 | 4 | 2022 |
| Testification of condorcet winners in dueling bandits B Haddenhorst, V Bengs, J Brandt, E Hüllermeier Uncertainty in Artificial Intelligence, 1195-1205, 2021 | 4 | 2021 |
| Online preselection with context information under the plackett-luce model AE Mesaoudi-Paul, V Bengs, E Hüllermeier arXiv preprint arXiv:2002.04275, 2020 | 4 | 2020 |
| On testing transitivity in online preference learning B Haddenhorst, V Bengs, E Hüllermeier Machine Learning 110, 2063-2084, 2021 | 3 | 2021 |
Eyke HüllermeierProfessor of Computer Science, Paderborn University在 upb.de 的电子邮件经过验证
