| Efficient and robust algorithms for adversarial linear contextual bandits G Neu, J Olkhovskaya Conference on Learning Theory, 3049-3068, 2020 | 48 | 2020 |
| Online learning in MDPs with linear function approximation and bandit feedback. G Neu, J Olkhovskaya Advances in Neural Information Processing Systems 34, 10407-10417, 2021 | 29 | 2021 |
| Lifting the information ratio: An information-theoretic analysis of thompson sampling for contextual bandits G Neu, I Olkhovskaia, M Papini, L Schwartz Advances in Neural Information Processing Systems 35, 9486-9498, 2022 | 13 | 2022 |
| First-and second-order bounds for adversarial linear contextual bandits J Olkhovskaya, J Mayo, T van Erven, G Neu, CY Wei Advances in Neural Information Processing Systems 36, 2024 | 8 | 2024 |
| Online influence maximization with local observations G Lugosi, G Neu, J Olkhovskaya Algorithmic Learning Theory, 557-580, 2019 | 8* | 2019 |
| Kernelized reinforcement learning with order optimal regret bounds S Vakili, J Olkhovskaya Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Improved Regret Bounds for Bandits with Expert Advice N Cesa-Bianchi, K Eldowa, E Esposito, J Olkhovskaya arXiv preprint arXiv:2406.16802, 2024 | | 2024 |
| Adversarial Contextual Bandits Go Kernelized G Neu, J Olkhovskaya, S Vakili International Conference on Algorithmic Learning Theory, 907-929, 2024 | | 2024 |
| Adversarial Contextual Bandits Go Kernelized G Neu, J Olkhovskaya, S Vakili arXiv preprint arXiv:2310.01609, 2023 | | 2023 |
| Analyzing Thompson Sampling for Contextual Bandits via the Lifted Information Ratio G Neu, J Olkhovskaya, M Papini, L Schwartz | | 2022 |
| Large-scale online learning under partial feedback I Olkhovskaia Universitat Pompeu Fabra, 2022 | | 2022 |
| Learning to maximize global influence from local observations G Lugosi, G Neu, J Olkhovskaya arXiv preprint arXiv:2109.11909, 2021 | | 2021 |
Gergely NeuArtificial Intelligence and Machine Learning group, Universitat Pompeu Fabra在 upf.edu 的电子邮件经过验证
