
 P.Auer, C.K.Chiang:
An algorithm with nearly optimal pseudoregret for both stochastic and adversarial bandits,
JMLR Workshop and Conference Proceedings Volume 49:
Proceedings of the 29th Conference on Learning Theory, COLT 2016, pp. 116120.
(pdf)
 P.Auer, C.K.Chiang, R.Ortner, M.Drugan:
Pareto Front Identification from Stochastic Bandit Feedback,
JMLR Workshop and Conference Proceedings Volume 51 : Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016.
(pdf)
 V.Gabillon, A.Lazaric, M.Ghavamzadeh, R.Ortner, P.Bartlett:
Improved Learning Complexity in Combinatorial Pure Exploration Bandits,
JMLR Workshop and Conference Proceedings Volume 51 : Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016.
(pdf)
 R.Ortner: Optimal Behavior is Easier to Learn than the Truth,
Minds and Machines, to appear
(open access pdf)
 K.Lakshmanan, R.Ortner, and D.Ryabko:
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning,
JMLR Workshop and Conference Proceedings Volume 37 : Proceedings of The 32nd International Conference on Machine Learning, ICML 2015.
(preprint pdf)
 R.Ortner, O.Maillard, and D.Ryabko:
Selecting NearOptimal Approximate State Representations in Reinforcement Learning,
Proceedings of the 25th International Conference on Algorithmic Learning Theory, ALT 2014.
Lecture Notes in Computer Science 8776, Springer 2014, pp. 140154.
(extended submission pdf)
 R.Ortner, D.Ryabko, P.Auer, and R.Munos:
Regret Bounds for Restless Markov Bandits,
Theoretical Computer Science 558, 6276 (2014)
(preprint pdf)
 R.Ortner:
Adaptive Aggregation for Reinforcement Learning in Average Reward Markov Decision Processes,
Ann.Oper.Res. 208 / 1, 321336 (2013)
(preprint pdf)
 R.Ortner, D.Ryabko, P.Auer, and R.Munos:
Regret Bounds for Restless Markov Bandits,
In: Proceedings of the 23th International Conference on Algorithmic Learning Theory, ALT 2012.
Lecture Notes in Computer Science 7568, Springer 2012, pp. 214228.
(preprint pdf)
 R.Ortner, D.Ryabko:
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning,
In: Advances in Neural Information Processing Systems 25 (2012), pp. 17721780.
(preprint pdf)
 O.Maillard, P.Nguyen, R.Ortner, and D.Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning,
JMLR Workshop and Conference Proceedings Volume 28 : Proceedings of The 30th International Conference on Machine Learning, ICML 2013, pp. 543551.
(corrected preprint pdf)
 P.Nguyen, O.Maillard, D.Ryabko, R.Ortner:
Competing with an Infinite Set of Models in Reinforcement Learning,
JMLR Workshop and Conference Proceedings Volume 31 : Proceedings of the 16th International Conference on Artificial Intelligence and Statistics, AISTATS 2013, pp. 463471.
(preprint pdf)

