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We stand with Ukraine

Ronald Ortner

Orcid: 0000-0001-6033-2208

According to our database¹, Ronald Ortner authored at least 41 papers between 2004 and 2023.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of three.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

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Links

On csauthors.net:

Bibliography

2023

Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms.

[BibT_eX]

[DOI]

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Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022

Decision Making Under Uncertainty and Reinforcement Learning - Theory and Algorithms

[BibT_eX]

[DOI]

Christos Dimitrakakis

,

Intelligent Systems Reference Library 223, Springer, ISBN: 978-3-031-07612-1, 2022

Predicting Packaging Sizes Using Machine Learning.

[BibT_eX]

[DOI]

Michael Heininger

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Oper. Res. Forum, 2022

Transfer in Reinforcement Learning via Regret Bounds for Learning Agents.

[BibT_eX]

[DOI]

Adrienne Tuynman

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CoRR, 2022

2021

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

[BibT_eX]

[DOI]

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Armin Fügenschuh

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Agnes Werner-Stark

Central Eur. J. Oper. Res., 2021

2020

Regret Bounds for Reinforcement Learning via Markov Chain Concentration.

[BibT_eX]

[DOI]

J. Artif. Intell. Res., 2020

2019

Autonomous exploration for navigating in non-stationary CMPs.

[BibT_eX]

[DOI]

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,

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Csaba Szepesvári

CoRR, 2019

Variational Regret Bounds for Reinforcement Learning.

[BibT_eX]

[DOI]

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Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Regret Bounds for Learning State Representations in Reinforcement Learning.

[BibT_eX]

[DOI]

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Alessandro Lazaric

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Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

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

[BibT_eX]

[DOI]

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Proceedings of the Conference on Learning Theory, 2019

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

[BibT_eX]

[DOI]

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Proceedings of the Conference on Learning Theory, 2019

2018

Guest Editors' Foreword.

[BibT_eX]

[DOI]

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Hans Ulrich Simon

Theor. Comput. Sci., 2018

A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions.

[BibT_eX]

[DOI]

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CoRR, 2018

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning.

[BibT_eX]

[DOI]

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,

Alessandro Lazaric

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Proceedings of the 35th International Conference on Machine Learning, 2018

2016

Optimal Behavior is Easier to Learn than the Truth.

[BibT_eX]

[DOI]

Minds Mach., 2016

Improved Learning Complexity in Combinatorial Pure Exploration Bandits.

[BibT_eX]

[DOI]

Victor Gabillon

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Alessandro Lazaric

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Mohammad Ghavamzadeh

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,

Peter L. Bartlett

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Pareto Front Identification from Stochastic Bandit Feedback.

[BibT_eX]

[DOI]

,

Chao-Kai Chiang

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,

Madalina M. Drugan

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

Forcing subarrangements in complete arrangements of pseudocircles.

[BibT_eX]

[DOI]

J. Comput. Geom., 2015

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning.

[BibT_eX]

[DOI]

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Proceedings of the 32nd International Conference on Machine Learning, 2015

2014

Regret bounds for restless Markov bandits.

[BibT_eX]

[DOI]

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Theor. Comput. Sci., 2014

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning.

[BibT_eX]

[DOI]

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Odalric-Ambrym Maillard

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Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013

Adaptive aggregation for reinforcement learning in average reward Markov decision processes.

[BibT_eX]

[DOI]

Ann. Oper. Res., 2013

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning.

[BibT_eX]

[DOI]

Odalric-Ambrym Maillard

,

,

,

Proceedings of the 30th International Conference on Machine Learning, 2013

Competing with an Infinite Set of Models in Reinforcement Learning.

[BibT_eX]

[DOI]

,

Odalric-Ambrym Maillard

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Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Online Regret Bounds for Undiscounted Continuous Reinforcement Learning.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

Mechanizing Induction.

[BibT_eX]

[DOI]

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Proceedings of the Inductive Logic, 2011

PAC-Bayesian Analysis of Contextual Bandits.

[BibT_eX]

[DOI]

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François Laviolette

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John Shawe-Taylor

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Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Online regret bounds for Markov decision processes with deterministic transitions.

[BibT_eX]

[DOI]

Theor. Comput. Sci., 2010

UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem.

[BibT_eX]

[DOI]

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Period. Math. Hung., 2010

Near-optimal Regret Bounds for Reinforcement Learning.

[BibT_eX]

[DOI]

,

,

J. Mach. Learn. Res., 2010

Exploiting Similarity Information in Reinforcement Learning - Similarity Models for Multi-Armed Bandits and MDPs.

[BibT_eX]

Proceedings of the ICAART 2010 - Proceedings of the International Conference on Agents and Artificial Intelligence, Volume 1, 2010

2008

Optimism in the Face of Uncertainty Should be Refutable.

[BibT_eX]

[DOI]

Minds Mach., 2008

Embeddability of arrangements of pseudocircles into the sphere.

[BibT_eX]

[DOI]

Eur. J. Comb., 2008

2007

Linear dependence of stationary distributions in ergodic Markov decision processes.

[BibT_eX]

[DOI]

Oper. Res. Lett., 2007

A new PAC bound for intersection-closed concept classes.

[BibT_eX]

[DOI]

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Mach. Learn., 2007

Improved Rates for the Stochastic Continuum-Armed Bandit Problem.

[BibT_eX]

[DOI]

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Csaba Szepesvári

Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

Pseudometrics for State Aggregation in Average Reward Markov Decision Processes.

[BibT_eX]

[DOI]

Proceedings of the Algorithmic Learning Theory, 18th International Conference, 2007

2006

Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning.

[BibT_eX]

[DOI]

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Proceedings of the Advances in Neural Information Processing Systems 19, 2006

2005

Combinations and Mixtures of Optimal Policies in Unichain Markov Decision Processes are Optimal

[BibT_eX]

[DOI]

CoRR, 2005

2004

On the Combinatorial Structure of Arrangements of Oriented Pseudocircles.

[BibT_eX]

[DOI]

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Electron. J. Comb., 2004

A Boosting Approach to Multiple Instance Learning.

[BibT_eX]

[DOI]

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Proceedings of the Machine Learning: ECML 2004, 2004

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