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Papers |
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Combinatorial Geometry: |
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- J.Linhart, R.Ortner:
On the Combinatorial Structure of Arrangements of Oriented Pseudocircles
Electron. J. Combin. 11 (2004),
Research Paper 30, 13 pp. (electronic).
(pdf)
- J.Linhart, R.Ortner:
An Arrangement of Pseudocircles not Realizable with Circles,
Beiträge Algebra Geom. Vol. 46, No. 2, pp. 351-356 (2005).
(preprint pdf)
- R.Ortner:
Embeddability of Arrangements of Pseudocircles into the Sphere,
European J. Combin. 29, pp. 457-469 (2008).
(corrected preprint pdf)
- R.Ortner:
Improved Upper Bounds on the Number of Vertices of Weight <= k
in Particular Arrangements of Pseudocircles,
24th European Workshop on Computational Geometry, pp. 35-38 (2008).
(corrected version pdf)
- J.Linhart, R.Ortner:
A Note on Convex Realizability of Arrangements of Pseudocircles,
Geombinatorics Vol XVIII, Issue 2, pp. 66-71 (2008).
(corrected version pdf)
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Random Walks: |
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Markov Decision Processes and Reinforcement Learning: |
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- P.Auer, R.Ortner:
Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning,
In: Advances in Neural Information Processing Systems 19 (2007), pp. 49-56.
(pdf)
- R.Ortner:
Linear Dependence of Stationary Distributions in Ergodic Markov Decision Processes
,
OR Letters 35 / 5, pp. 619-626 (2007).
(preprint pdf)
- P.Auer, R.Ortner, and C. Szepesvári:
Improved Rates for the Stochastic Continuum-Armed Bandit Problem,
In: Learning Theory, 20th Annual Conference on Learning Theory, COLT 2007.
Lecture Notes in Computer Science 4539, Springer 2007, pp. 454-468.
(preprint pdf)
- R.Ortner:
Pseudometrics for State Aggregation in Average Reward Markov Decision Processes,
In: Proceedings of the 18th International Conference on Algorithmic Learning Theory, ALT 2007.
Lecture Notes in Computer Science 4754, Springer 2007, pp. 373-387.
(extended version pdf)
- R.Ortner:
Online Regret Bounds for Markov Decision Processes with Deterministic Transitions,
In: Proceedings of the 19th International Conference on Algorithmic Learning Theory, ALT 2008.
Lecture Notes in Computer Science 5254, Springer 2008, pp. 123-137.
(preprint pdf), (improved journal version pdf)
- R.Ortner: Optimism in the Face of Uncertainty Should be Refutable,
Minds and Machines 18 / 4, pp. 521 - 526 (2008).
(preprint pdf)
- P.Auer, T.Jaksch, and R.Ortner:
Near-optimal Regret Bounds for Reinforcement Learning,
In: Advances in neural information processing systems 21 (2009), pp. 89-96.
- R.Ortner: Online Regret Bounds for Markov Decision Processes with Deterministic Transitions,
Theoretical Computer Science 411 / 29-30, pp. 2684-2695 (2010).
(pdf)
- R.Ortner:
Exploiting Similarity Information in Reinforcement Learning.
Similarity Models for Multi-Armed Bandits and MDPs.
In: ICAART 2010. Proceedings of the 2nd International Conference on Agents and Artificial Intelligence,
Volume 1 (Artificial Intelligence), pp. 203-210.
(pdf)
- T.Jaksch, R.Ortner, and P.Auer:
Near-optimal Regret Bounds for Reinforcement Learning,
J.Mach.Learn.Res. 11, pp. 1563-1600 (2010).
- P.Auer and R.Ortner:
UCB Revisited: Improved Regret Bounds for the Stochastic Multi-Armed Bandit Problem,
Period.Math.Hungar. 61 / 1-2, pp. 55-65 (2010).
(corrected preprint pdf)
- R.Ortner:
Adaptive Aggregation for Reinforcement Learning in Average Reward Markov Decision Processes,
to appear in: Ann.Oper.Res.
(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. 214-228.
(preprint pdf)
- R.Ortner, D.Ryabko:
Online Regret Bounds for Undiscounted Continuous Reinforcement Learning,
In: Advances in Neural Information Processing Systems 25 (2012), pp. 1772-1780.
(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. 543-551.
(corrected preprint pdf)
- P.Nguyen, O.Maillard, D.Ryabko, R.Ortner:
Competing with an Infinite Set of Models in Reinforcement Learning,
accepted for AISTATS 2013.
(preprint pdf)
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None of the above: |
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- P.Auer, R.Ortner:
A New PAC-bound for Intersection-closed Concept Classes,
In: Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004.
Lecture Notes in Computer Science 3120, Springer 2004, pp. 408-414.
(extended version pdf)
- P.Auer, R.Ortner:
A Boosting Approach to Multiple Instance Learning,
In: Machine Learning, 15th European Conference on Machine Learning, ECML 2004.
Lecture Notes in Artificial Intelligence 3201, Springer 2004, pp. 63-74.
(extended version pdf)
- P.Auer, R.Ortner:
A New PAC-bound for Intersection-closed Concept Classes,
In: Machine Learning, Vol. 66, No. 2-3, pp. 151-163 (2007).
(preprint pdf)
- M.Antenreiter, R. Ortner, and Peter Auer:
Combining Classifiers for Improved Multilabel Image Classification,
In: Learning from Multi-label Data, MLD Workshop at ECML 2009, pp. 16-27.
(pdf)
- H.Leitgeb and R.Ortner:
Mechanizing Induction,
In: Dov Gabbay, Stephan Hartmann, John Woods (ed.), Handbook for the History of Logic, Vol. 10: Inductive Logic. Elsevier. pp 719-772, 2011.
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