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ORCIDRonald Ortner
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2020 – today
- 2023
[j17]Thomas Michel, Hossein Hajiabolhassan, Ronald Ortner:
Regret Bounds for Satisficing in Multi-Armed Bandit Problems. Trans. Mach. Learn. Res. 2023 (2023)- [c23]Pratik Gajane, Peter Auer, Ronald Ortner:
Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms. IJCAI 2023: 3714-3722 - 2022
- [b1]Christos Dimitrakakis, Ronald Ortner:
Decision Making Under Uncertainty and Reinforcement Learning - Theory and Algorithms. Intelligent Systems Reference Library 223, Springer 2022, ISBN 978-3-031-07612-1, pp. 1-238 - [j16]Michael Heininger, Ronald Ortner
:
Predicting Packaging Sizes Using Machine Learning. Oper. Res. Forum 3(3) (2022) - [i12]Adrienne Tuynman, Ronald Ortner:
Transfer in Reinforcement Learning via Regret Bounds for Learning Agents. CoRR abs/2202.01182 (2022) - 2021
- [j15]Peter Auer, György Dósa, Tibor Dulai, Armin Fügenschuh, Peggy Näser, Ronald Ortner, Agnes Werner-Stark:
A new heuristic and an exact approach for a production planning problem. Central Eur. J. Oper. Res. 29(3): 1079-1113 (2021) - 2020
- [j14]Ronald Ortner:
Regret Bounds for Reinforcement Learning via Markov Chain Concentration. J. Artif. Intell. Res. 67: 115-128 (2020)
2010 – 2019
- 2019
- [c22]Peter Auer, Pratik Gajane, Ronald Ortner:
Adaptively Tracking the Best Bandit Arm with an Unknown Number of Distribution Changes. COLT 2019: 138-158 - [c21]Peter Auer, Yifang Chen, Pratik Gajane, Chung-Wei Lee, Haipeng Luo, Ronald Ortner, Chen-Yu Wei:
Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information. COLT 2019: 159-163 - [c20]Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard:
Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS 2019: 12717-12727 - [c19]Ronald Ortner, Pratik Gajane, Peter Auer:
Variational Regret Bounds for Reinforcement Learning. UAI 2019: 81-90 - [i11]Pratik Gajane, Ronald Ortner, Peter Auer:
Variational Regret Bounds for Reinforcement Learning. CoRR abs/1905.05857 (2019) - [i10]Pratik Gajane, Ronald Ortner, Peter Auer, Csaba Szepesvári:
Autonomous exploration for navigating in non-stationary CMPs. CoRR abs/1910.08446 (2019) - 2018
- [j13]Ronald Ortner, Hans Ulrich Simon:
Guest Editors' Foreword. Theor. Comput. Sci. 742: 1-2 (2018) - [c18]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner:
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. ICML 2018: 1573-1581 - [i9]Ronan Fruit, Matteo Pirotta, Alessandro Lazaric, Ronald Ortner:
Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning. CoRR abs/1802.04020 (2018) - [i8]Pratik Gajane, Ronald Ortner, Peter Auer:
A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions. CoRR abs/1805.10066 (2018) - [i7]Ronald Ortner:
Regret Bounds for Reinforcement Learning via Markov Chain Concentration. CoRR abs/1808.01813 (2018) - 2016
- [j12]Ronald Ortner:
Optimal Behavior is Easier to Learn than the Truth. Minds Mach. 26(3): 243-252 (2016) - [c17]Peter Auer, Chao-Kai Chiang, Ronald Ortner, Madalina M. Drugan:
Pareto Front Identification from Stochastic Bandit Feedback. AISTATS 2016: 939-947 - [c16]Victor Gabillon, Alessandro Lazaric, Mohammad Ghavamzadeh, Ronald Ortner, Peter L. Bartlett:
Improved Learning Complexity in Combinatorial Pure Exploration Bandits. AISTATS 2016: 1004-1012 - [e1]Ronald Ortner, Hans Ulrich Simon, Sandra Zilles:
Algorithmic Learning Theory - 27th International Conference, ALT 2016, Bari, Italy, October 19-21, 2016, Proceedings. Lecture Notes in Computer Science 9925, 2016, ISBN 978-3-319-46378-0 [contents] - 2015
- [j11]Ronald Ortner:
Forcing subarrangements in complete arrangements of pseudocircles. J. Comput. Geom. 6(1): 235-248 (2015) - [c15]K. Lakshmanan, Ronald Ortner, Daniil Ryabko:
Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning. ICML 2015: 524-532 - 2014
- [j10]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos:
Regret bounds for restless Markov bandits. Theor. Comput. Sci. 558: 62-76 (2014) - [c14]Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko:
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. ALT 2014: 140-154 - [i6]Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko:
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. CoRR abs/1405.2652 (2014) - 2013
- [j9]Ronald Ortner:
Adaptive aggregation for reinforcement learning in average reward Markov decision processes. Ann. Oper. Res. 208(1): 321-336 (2013) - [c13]Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner:
Competing with an Infinite Set of Models in Reinforcement Learning. AISTATS 2013: 463-471 - [c12]Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. ICML (1) 2013: 543-551 - [i5]Ronald Ortner, Daniil Ryabko:
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. CoRR abs/1302.2550 (2013) - [i4]Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. CoRR abs/1302.2553 (2013) - 2012
- [c11]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos:
Regret Bounds for Restless Markov Bandits. ALT 2012: 214-228 - [c10]Ronald Ortner, Daniil Ryabko:
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning. NIPS 2012: 1772-1780 - [i3]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos:
Regret Bounds for Restless Markov Bandits. CoRR abs/1209.2693 (2012) - 2011
- [c9]Yevgeny Seldin, Peter Auer, François Laviolette, John Shawe-Taylor, Ronald Ortner:
PAC-Bayesian Analysis of Contextual Bandits. NIPS 2011: 1683-1691 - [r1]Ronald Ortner, Hannes Leitgeb:
Mechanizing Induction. Inductive Logic 2011: 719-772 - 2010
- [j8]Thomas Jaksch, Ronald Ortner, Peter Auer:
Near-optimal Regret Bounds for Reinforcement Learning. J. Mach. Learn. Res. 11: 1563-1600 (2010) - [j7]Peter Auer, Ronald Ortner:
UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem. Period. Math. Hung. 61(1-2): 55-65 (2010) - [j6]Ronald Ortner:
Online regret bounds for Markov decision processes with deterministic transitions. Theor. Comput. Sci. 411(29-30): 2684-2695 (2010) - [c8]Ronald Ortner:
Exploiting Similarity Information in Reinforcement Learning - Similarity Models for Multi-Armed Bandits and MDPs. ICAART (1) 2010: 203-210
2000 – 2009
- 2008
- [j5]Ronald Ortner:
Embeddability of arrangements of pseudocircles into the sphere. Eur. J. Comb. 29(2): 457-469 (2008) - [j4]Ronald Ortner:
Optimism in the Face of Uncertainty Should be Refutable. Minds Mach. 18(4): 521-526 (2008) - [c7]Ronald Ortner:
Online Regret Bounds for Markov Decision Processes with Deterministic Transitions. ALT 2008: 123-137 - [c6]Peter Auer, Thomas Jaksch, Ronald Ortner:
Near-optimal Regret Bounds for Reinforcement Learning. NIPS 2008: 89-96 - 2007
- [j3]Peter Auer, Ronald Ortner:
A new PAC bound for intersection-closed concept classes. Mach. Learn. 66(2-3): 151-163 (2007) - [j2]Ronald Ortner:
Linear dependence of stationary distributions in ergodic Markov decision processes. Oper. Res. Lett. 35(5): 619-626 (2007) - [c5]Ronald Ortner:
Pseudometrics for State Aggregation in Average Reward Markov Decision Processes. ALT 2007: 373-387 - [c4]Peter Auer, Ronald Ortner, Csaba Szepesvári:
Improved Rates for the Stochastic Continuum-Armed Bandit Problem. COLT 2007: 454-468 - 2006
- [c3]Peter Auer, Ronald Ortner:
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning. NIPS 2006: 49-56 - 2005
- [i2]Ronald Ortner:
Combinations and Mixtures of Optimal Policies in Unichain Markov Decision Processes are Optimal. CoRR abs/math/0508319 (2005) - [i1]Ronald Ortner:
Embeddability of Arrangements of Pseudocircles into the Sphere. CoRR abs/math/0508320 (2005) - 2004
- [j1]Johann Linhart, Ronald Ortner:
On the Combinatorial Structure of Arrangements of Oriented Pseudocircles. Electron. J. Comb. 11(1) (2004) - [c2]Peter Auer, Ronald Ortner:
A New PAC Bound for Intersection-Closed Concept Classes. COLT 2004: 408-414 - [c1]Peter Auer, Ronald Ortner:
A Boosting Approach to Multiple Instance Learning. ECML 2004: 63-74
Coauthor Index
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