Research output

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2019
Published
Regret Bounds for Learning State Representations in Reinforcement Learning
Ortner, R., Pirotta, M., Lazaric, A., Fruit, R. & Maillard, O-A., Dec 2019.
Research output: Contribution to conference › Poster › Research › peer-review
Published
Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes
Auer, P., Gajane, P. & Ortner, R., 27 Jun 2019.
Research output: Contribution to conference › Poster › Research › peer-review
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. 138-158
Research 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
Published
Variational Regret Bounds for Reinforcement Learning
Ortner, R., Gajane, P. & Auer, P., 2019, Proceedings of The 35th Uncertainty in Artificial Intelligence Conference, UAI 2019. p. 81-90
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
Published
Variational Regret Bounds for Reinforcement Learning
Ortner, R., Gajane, P. & Auer, P., 2019.
Research output: Contribution to conference › Paper › peer-review
2018
Published
Online learning with randomized feedback graphs for optimal PUE attacks in cognitive radio networks
Dabaghchian, M., Alipour-Fanid, A., Zeng, K., Wang, Q. & Auer, P., 1 Oct 2018, In: IEEE ACM transactions on networking. 26, 5, p. 2268-2281 14 p., 8466108.
Research output: Contribution to journal › Article › 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
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

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Research Portal

Research output

Regret Bounds for Learning State Representations in Reinforcement Learning

Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes

Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information

Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes

Regret Bounds for Learning State Representations in Reinforcement Learning

Variational Regret Bounds for Reinforcement Learning

Variational Regret Bounds for Reinforcement Learning

Online learning with randomized feedback graphs for optimal PUE attacks in cognitive radio networks

A Sliding-Window Approach for Reinforcement Learning in MDPs with Arbitrarily Changing Rewards and Transitions.

Adaptively Tracking the Best Arm with an Unknown Number of Distribution Changes

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