متابعة
Viktor Bengs
Viktor Bengs
LMU Munich
بريد إلكتروني تم التحقق منه على lmu.de
عنوان
عدد مرات الاقتباسات
عدد مرات الاقتباسات
السنة
A survey of reinforcement learning from human feedback‏
T Kaufmann, P Weng, V Bengs, E Hüllermeier‏
arXiv preprint arXiv:2312.14925 10, 2023‏
1262023
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
1222021
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
612022
Pitfalls of epistemic uncertainty quantification through loss minimisation‏
V Bengs, E Hüllermeier, W Waegeman‏
Advances in Neural Information Processing Systems 35, 29205-29216, 2022‏
49*2022
On second-order scoring rules for epistemic uncertainty quantification‏
V Bengs, E Hüllermeier, W Waegeman‏
International Conference on Machine Learning, 2078-2091, 2023‏
282023
Stochastic contextual dueling bandits under linear stochastic transitivity models‏
V Bengs, A Saha, E Hüllermeier‏
International Conference on Machine Learning, 1764-1786, 2022‏
272022
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
262024
Second-order uncertainty quantification: A distance-based approach‏
Y Sale, V Bengs, M Caprio, E Hüllermeier‏
arXiv preprint arXiv:2312.00995, 2023‏
242023
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
142020
Uniform approximation in classical weak convergence theory‏
V Bengs, H Holzmann‏
arXiv preprint arXiv:1903.09864, 2019‏
142019
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
132022
Is epistemic uncertainty faithfully represented by evidential deep learning methods?‏
M Jürgens, N Meinert, V Bengs, E Hüllermeier, W Waegeman‏
arXiv preprint arXiv:2402.09056, 2024‏
92024
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
92021
A survey of reinforcement learning from human feedback. arXiv 2023‏
T Kaufmann, P Weng, V Bengs, E Hüllermeier‏
arXiv preprint arXiv:2312.14925, 0‏
9
On the calibration of probabilistic classifier sets‏
T Mortier, V Bengs, E Hüllermeier, S Luca, W Waegeman‏
International Conference on Artificial Intelligence and Statistics, 8857-8870, 2023‏
72023
Non-stationary dueling bandits‏
P Kolpaczki, V Bengs, E Hüllermeier‏
arXiv preprint arXiv:2202.00935, 2022‏
72022
A survey of reinforcement learning from human feedback (2024)‏
T Kaufmann, P Weng, V Bengs, E Hüllermeier‏
URL: https://arxiv. org/abs/2312.14925, 0‏
7
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
62023
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
62022
يتعذر على النظام إجراء العملية في الوقت الحالي. عاود المحاولة لاحقًا.
مقالات 1–20