Ronald Ortner
Research output
- 2016
- Published
Pareto Front Identification from Stochastic Bandit Feedback
Auer, P., Chiang, C.-K., Ortner, R. & Drugan, M., 2016, Proceedings of the Nineteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2016. p. 939-947 (JMLR Workshop and Conference Proceedings).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Improved Learning Complexity in Combinatorial Pure Exploration Bandits
Gabillon, V., Lazaric, A., Ghavamzadeh, M., Ortner, R. & Bartlett, P., 10 May 2016.Research output: Contribution to conference › Poster › Research › peer-review
- 2018
- Published
Adaptively Tracking the Best Arm with an Unknown Number of Distribution Changes
Auer, P., Gajane, P. & Ortner, R., 2018.Research output: Contribution to conference › Paper › peer-review
- Published
Adaptively Tracking the Best Arm with an Unknown Number of Distribution Changes
Auer, P., Gajane, P. & Ortner, R., 2018.Research output: Contribution to conference › Poster › Research › peer-review
- Published
A Sliding-Window Approach for Reinforcement Learning in MDPs with Arbitrarily Changing Rewards and Transitions.
Gajane, P., Ortner, R. & Auer, P., 2018.Research output: Contribution to conference › Paper › peer-review
- Published
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning
Fruit, R., Pirotta, M., Lazaric, A. & Ortner, R., 2018, Proceedings of the 35th International Conference on Machine Learning, ICML 2018. Vol. PMLR 80. p. 1578-1586Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Guest Editors' Foreword
Ortner, R. & Ulrich Simon, H., 2018, In: Theoretical Computer Science. 742Research output: Contribution to journal › Article › Research
- 2019
- Published
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information
Auer, P., Chen, Y., Gajane, P., Lee, C.-W., Luo, H., Ortner, R. & Wei, C.-Y., 2019.Research output: Contribution to conference › Abstract › peer-review
- Published
Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes
Auer, P., Gajane, P. & Ortner, R., 2019, Proceedings of the 32nd Conference on Learning Theory, COLT 2019. p. 138-158Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- E-pub ahead of print
Regret Bounds for Learning State Representations in Reinforcement Learning
Ortner, R., Pirotta, M., Lazaric, A., Fruit, R. & Maillard, O.-A., 2019, (E-pub ahead of print) Advances in Neural Information Processing Systems. Vol. 32. p. 12717 12727 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
