Ronald Ortner

Orcid: 0000-0001-6033-2208

According to our database1, Ronald Ortner authored at least 42 papers between 2004 and 2023.

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Bibliography

2023
Regret Bounds for Satisficing in Multi-Armed Bandit Problems.
Trans. Mach. Learn. Res., 2023

Autonomous Exploration for Navigating in MDPs Using Blackbox RL Algorithms.
Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023

2022
Decision Making Under Uncertainty and Reinforcement Learning - Theory and Algorithms
Intelligent Systems Reference Library 223, Springer, ISBN: 978-3-031-07612-1, 2022

Predicting Packaging Sizes Using Machine Learning.
Oper. Res. Forum, 2022

Transfer in Reinforcement Learning via Regret Bounds for Learning Agents.
CoRR, 2022

2021
A new heuristic and an exact approach for a production planning problem.
Central Eur. J. Oper. Res., 2021

2020
Regret Bounds for Reinforcement Learning via Markov Chain Concentration.
J. Artif. Intell. Res., 2020

2019
Autonomous exploration for navigating in non-stationary CMPs.
CoRR, 2019

Variational Regret Bounds for Reinforcement Learning.
Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Regret Bounds for Learning State Representations in Reinforcement Learning.
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.
Proceedings of the Conference on Learning Theory, 2019

Achieving Optimal Dynamic Regret for Non-stationary Bandits without Prior Information.
Proceedings of the Conference on Learning Theory, 2019

2018
Guest Editors' Foreword.
Theor. Comput. Sci., 2018

A Sliding-Window Algorithm for Markov Decision Processes with Arbitrarily Changing Rewards and Transitions.
CoRR, 2018

Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning.
Proceedings of the 35th International Conference on Machine Learning, 2018

2016
Optimal Behavior is Easier to Learn than the Truth.
Minds Mach., 2016

Improved Learning Complexity in Combinatorial Pure Exploration Bandits.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

Pareto Front Identification from Stochastic Bandit Feedback.
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015
Forcing subarrangements in complete arrangements of pseudocircles.
J. Comput. Geom., 2015

Improved Regret Bounds for Undiscounted Continuous Reinforcement Learning.
Proceedings of the 32nd International Conference on Machine Learning, 2015

2014
Regret bounds for restless Markov bandits.
Theor. Comput. Sci., 2014

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning.
Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013
Adaptive aggregation for reinforcement learning in average reward Markov decision processes.
Ann. Oper. Res., 2013

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning.
Proceedings of the 30th International Conference on Machine Learning, 2013

Competing with an Infinite Set of Models in Reinforcement Learning.
Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning.
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.
Proceedings of the Inductive Logic, 2011

PAC-Bayesian Analysis of Contextual Bandits.
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.
Theor. Comput. Sci., 2010

UCB revisited: Improved regret bounds for the stochastic multi-armed bandit problem.
Period. Math. Hung., 2010

Near-optimal Regret Bounds for Reinforcement Learning.
J. Mach. Learn. Res., 2010

Exploiting Similarity Information in Reinforcement Learning - Similarity Models for Multi-Armed Bandits and MDPs.
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.
Minds Mach., 2008

Embeddability of arrangements of pseudocircles into the sphere.
Eur. J. Comb., 2008

2007
Linear dependence of stationary distributions in ergodic Markov decision processes.
Oper. Res. Lett., 2007

A new PAC bound for intersection-closed concept classes.
Mach. Learn., 2007

Improved Rates for the Stochastic Continuum-Armed Bandit Problem.
Proceedings of the Learning Theory, 20th Annual Conference on Learning Theory, 2007

Pseudometrics for State Aggregation in Average Reward Markov Decision Processes.
Proceedings of the Algorithmic Learning Theory, 18th International Conference, 2007

2006
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning.
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
CoRR, 2005

2004
On the Combinatorial Structure of Arrangements of Oriented Pseudocircles.
Electron. J. Comb., 2004

A Boosting Approach to Multiple Instance Learning.
Proceedings of the Machine Learning: ECML 2004, 2004


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