Research output

11 - 20 out of 77Page size: 10

Sort by: Publication date

2021
Published
A new heuristic and an exact approach for a production planning problem
Auer, P., Dósa, G., Dulai, T., Fügenschuh, A., Näser, P., Ortner, R. & Werner-Starkne, A., Sept 2021, In: Central European Journal of Operations Research. 29, 3, p. 1079-1113 35 p.
Research output: Contribution to journal › Article › Research › peer-review
Published
Regret Bounds for Reinforcement Learning via Markov Chain Concentration
Ortner, R., 26 Aug 2021.
Research output: Contribution to conference › Poster › Research › peer-review
2020
Published
Regret Bounds for Reinforcement Learning via Markov Chain Concentration
Ortner, R., 23 Jan 2020, In: The journal of artificial intelligence research. 67.2020, 1, p. 115-128 14 p.
Research output: Contribution to journal › Article › Research › peer-review
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

Previous 1 2 3 4 5 6 7 8 Next

Research Portal

Research output

A new heuristic and an exact approach for a production planning problem

Regret Bounds for Reinforcement Learning via Markov Chain Concentration

Regret Bounds for Reinforcement Learning via Markov Chain Concentration

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

Advanced filters