Ronald Ortner
Research output
- 2022
- Published
Predicting Packaging Sizes Using Machine Learning
Heininger, M. & Ortner, R., 22 Aug 2022, In: Operations research forum. 43.2022, 3, 14 p., 43.Research output: Contribution to journal › Article › Research › peer-review
- 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. 138-158Research 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
- Published
Variational Regret Bounds for Reinforcement Learning
Ortner, R., Gajane, P. & Auer, P., 2019.Research output: Contribution to conference › Paper › peer-review
