| On formalizing fairness in prediction with machine learning P Gajane, M Pechenizkiy the 5th Workshop on Fairness, Accountability, and Transparency in Machine …, 2018 | 267 | 2018 |
| Adaptively tracking the best bandit arm with an unknown number of distribution changes P Auer, P Gajane, R Ortner Conference on Learning Theory, 138-158, 2019 | 122 | 2019 |
| Variational regret bounds for reinforcement learning R Ortner, P Gajane, P Auer Uncertainty in Artificial Intelligence, 81-90, 2020 | 65 | 2020 |
| A sliding-window algorithm for markov decision processes with arbitrarily changing rewards and transitions P Gajane, R Ortner, P Auer Lifelong Learning: A Reinforcement Learning Approach Workshop at FAIM, 2018 | 45 | 2018 |
| A relative exponential weighing algorithm for adversarial utility-based dueling bandits P Gajane, T Urvoy, F Clérot International Conference on Machine Learning, 218-227, 2015 | 42 | 2015 |
| Corrupt bandits for preserving local privacy P Gajane, T Urvoy, E Kaufmann Algorithmic Learning Theory, 387-412, 2018 | 39 | 2018 |
| Achieving optimal dynamic regret for non-stationary bandits without prior information P Auer, Y Chen, P Gajane, CW Lee, H Luo, R Ortner, CY Wei Conference on Learning Theory, 159-163, 2019 | 29 | 2019 |
| Adaptively tracking the best arm with an unknown number of distribution changes P Auer, P Gajane, R Ortner European Workshop on Reinforcement Learning 14, 375, 2018 | 27 | 2018 |
| Corrupt bandits P Gajane, T Urvoy, E Kaufmann EWRL, 2016 | 15 | 2016 |
| Survey on fair reinforcement learning: Theory and practice P Gajane, A Saxena, M Tavakol, G Fletcher, M Pechenizkiy arXiv preprint arXiv:2205.10032, 2022 | 10 | 2022 |
| Utility-based dueling bandits as a partial monitoring game P Gajane, T Urvoy arXiv preprint arXiv:1507.02750, 2015 | 6 | 2015 |
| Gambler bandits and the regret of being ruined FS Perotto, S Vakili, P Gajane, Y Faghan, M Bourgais 20th International Conference on Autonomous Agents and Multiagent Systems …, 2021 | 4 | 2021 |
| Autonomous exploration for navigating in non-stationary CMPs P Gajane, R Ortner, P Auer, C Szepesvari arXiv preprint arXiv:1910.08446, 2019 | 4 | 2019 |
| Counterfactual learning for machine translation: Degeneracies and solutions C Lawrence, P Gajane, S Riezler arXiv preprint arXiv:1711.08621, 2017 | 4 | 2017 |
| Lemon: Alternative sampling for more faithful explanation through local surrogate models D Collaris, P Gajane, J Jorritsma, JJ van Wijk, M Pechenizkiy International Symposium on Intelligent Data Analysis, 77-90, 2023 | 3 | 2023 |
| Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning J Li, P Gajane 16th European Workshop on Reinforcement Learning (EWRL), 2023 | 3 | 2023 |
| The impact of batch learning in stochastic linear bandits D Provodin, P Gajane, M Pechenizkiy, M Kaptein 2022 IEEE International Conference on Data Mining (ICDM), 1149-1154, 2022 | 3 | 2022 |
| Corrupt bandits for privacy preserving input P Gajane, T Urvoy, E Kaufmann arXiv preprint arXiv:1708.05033, 2017 | 3 | 2017 |
| The impact of batch learning in stochastic bandits D Provodin, P Gajane, M Pechenizkiy, M Kaptein Workshop on Ecological Theory of Reinforcement Learning, 2021 | 2 | 2021 |
| A Sliding-Window Approach for Reinforcement Learning in MDPs with Arbitrarily Changing Rewards and Transitions. P Gajane, R Ortner, P Auer | 2 | 2018 |
Mykola PechenizkiyEindhoven University of TechnologyVerified email at tue.nl
