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Rémi Munos
2010 – today
- 2013
[j19]Mohammad Gheshlaghi Azar, Rémi Munos, Hilbert J. Kappen: Minimax PAC bounds on the sample complexity of reinforcement learning with a generative model. Machine Learning 91(3): 325-349 (2013)- [i7]Amir Sani, Alessandro Lazaric, Rémi Munos: Risk-Aversion in Multi-armed Bandits. CoRR abs/1301.1936 (2013)
- [i6]Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko: Selecting the State-Representation in Reinforcement Learning. CoRR abs/1302.2552 (2013)
- 2012
- [j18]Alessandro Lazaric, Rémi Munos: Learning with stochastic inputs and adversarial outputs. J. Comput. Syst. Sci. 78(5): 1516-1537 (2012)
- [j17]Lucian Busoniu, Rémi Munos: Optimistic planning for Markov decision processes. Journal of Machine Learning Research - Proceedings Track 22: 182-189 (2012)
- [j16]Alexandra Carpentier, Rémi Munos: Bandit Theory meets Compressed Sensing for high dimensional Stochastic Linear Bandit. Journal of Machine Learning Research - Proceedings Track 22: 190-198 (2012)
- [c51]Emilie Kaufmann, Nathaniel Korda, Rémi Munos: Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis. ALT 2012: 199-213
- [c50]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos: Regret Bounds for Restless Markov Bandits. ALT 2012: 214-228
- [c49]Alexandra Carpentier, Rémi Munos: Minimax Number of Strata for Online Stratified Sampling Given Noisy Samples. ALT 2012: 229-244
- [c48]Mohammad Gheshlaghi Azar, Rémi Munos, Bert Kappen: On the Sample Complexity of Reinforcement Learning with a Generative Model . ICML 2012
- [c47]Alexandra Carpentier, Rémi Munos: Adaptive Stratified Sampling for Monte-Carlo integration of Differentiable functions. NIPS 2012: 251-259
- [c46]Joan Fruitet, Alexandra Carpentier, Rémi Munos, Maureen Clerc: Bandit Algorithms boost Brain Computer Interfaces for motor-task selection of a brain-controlled button. NIPS 2012: 458-466
- [c45]Amir Sani, Alessandro Lazaric, Rémi Munos: Risk-Aversion in Multi-armed Bandits. NIPS 2012: 3284-3292
- [i5]Emilie Kaufmann, Nathaniel Korda, Rémi Munos: Thompson Sampling: An Optimal Finite Time Analysis. CoRR abs/1205.4217 (2012)
- [i4]Ronald Ortner, Daniil Ryabko, Peter Auer, Rémi Munos: Regret Bounds for Restless Markov Bandits. CoRR abs/1209.2693 (2012)
- 2011
- [j15]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári: X-Armed Bandits. Journal of Machine Learning Research 12: 1655-1695 (2011)
- [j14]Odalric-Ambrym Maillard, Rémi Munos: Adaptive Bandits: Towards the best history-dependent strategy. Journal of Machine Learning Research - Proceedings Track 15: 570-578 (2011)
- [j13]Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz: A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. Journal of Machine Learning Research - Proceedings Track 19: 497-514 (2011)
- [j12]Sébastien Bubeck, Rémi Munos, Gilles Stoltz: Pure exploration in finitely-armed and continuous-armed bandits. Theor. Comput. Sci. 412(19): 1832-1852 (2011)
- [c44]Alexandra Carpentier, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos, Peter Auer: Upper-Confidence-Bound Algorithms for Active Learning in Multi-armed Bandits. ALT 2011: 189-203
- [c43]Matthew W. Hoffman, Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos: Regularized Least Squares Temporal Difference Learning with Nested ℓ2 and ℓ1 Penalization. EWRL 2011: 102-114
- [c42]Mohammad Ghavamzadeh, Alessandro Lazaric, Rémi Munos, Matthew W. Hoffman: Finite-Sample Analysis of Lasso-TD. ICML 2011: 1177-1184
- [c41]Rémi Munos: Optimistic Optimization of a Deterministic Function without the Knowledge of its Smoothness. NIPS 2011: 783-791
- [c40]Alexandra Carpentier, Rémi Munos: Finite Time Analysis of Stratified Sampling for Monte Carlo. NIPS 2011: 1278-1286
- [c39]Alexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos: Sparse Recovery with Brownian Sensing. NIPS 2011: 1782-1790
- [c38]Mohammad Gheshlaghi Azar, Rémi Munos, Mohammad Ghavamzadeh, Hilbert J. Kappen: Speedy Q-Learning. NIPS 2011: 2411-2419
- [c37]Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko: Selecting the State-Representation in Reinforcement Learning. NIPS 2011: 2627-2635
- 2010
- [j11]Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, Mohammad Ghavamzadeh: Finite-sample Analysis of Bellman Residual Minimization. Journal of Machine Learning Research - Proceedings Track 13: 299-314 (2010)
- [c36]Jean-Yves Audibert, Sébastien Bubeck, Rémi Munos: Best Arm Identification in Multi-Armed Bandits. COLT 2010: 41-53
- [c35]
- [c34]Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos: Analysis of a Classification-based Policy Iteration Algorithm. ICML 2010: 607-614
- [c33]Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi Munos: Finite-Sample Analysis of LSTD. ICML 2010: 615-622
- [c32]Amir Massoud Farahmand, Rémi Munos, Csaba Szepesvári: Error Propagation for Approximate Policy and Value Iteration. NIPS 2010: 568-576
- [c31]Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos: LSTD with Random Projections. NIPS 2010: 721-729
- [c30]Odalric-Ambrym Maillard, Rémi Munos: Scrambled Objects for Least-Squares Regression. NIPS 2010: 1549-1557
- [c29]Odalric-Ambrym Maillard, Rémi Munos: Online Learning in Adversarial Lipschitz Environments. ECML/PKDD (2) 2010: 305-320
- [i3]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári: X-Armed Bandits. CoRR abs/1001.4475 (2010)
2000 – 2009
- 2009
- [j10]Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári: Exploration-exploitation tradeoff using variance estimates in multi-armed bandits. Theor. Comput. Sci. 410(19): 1876-1902 (2009)
- [c28]Sébastien Bubeck, Rémi Munos, Gilles Stoltz: Pure Exploration in Multi-armed Bandits Problems. ALT 2009: 23-37
- [c27]
- [c26]Jean-Yves Audibert, Peter Auer, Alessandro Lazaric, Rémi Munos, Daniil Ryabko, Csaba Szepesvári: Workshop summary: On-line learning with limited feedback. ICML 2009: 168
- [c25]Pierre-Arnaud Coquelin, Romain Deguest, Rémi Munos: Sensitivity analysis in HMMs with application to likelihood maximization. NIPS 2009: 387-395
- [c24]
- 2008
- [j9]Rémi Munos, Csaba Szepesvári: Finite-Time Bounds for Fitted Value Iteration. Journal of Machine Learning Research 9: 815-857 (2008)
- [j8]András Antos, Csaba Szepesvári, Rémi Munos: Learning near-optimal policies with Bellman-residual minimization based fitted policy iteration and a single sample path. Machine Learning 71(1): 89-129 (2008)
- [c23]Raphaël Maîtrepierre, Jérémie Mary, Rémi Munos: Adaptive play in Texas Hold'em Poker. ECAI 2008: 458-462
- [c22]
- [c21]Sébastien Bubeck, Rémi Munos, Gilles Stoltz, Csaba Szepesvári: Online Optimization in X-Armed Bandits. NIPS 2008: 201-208
- [c20]Pierre-Arnaud Coquelin, Romain Deguest, Rémi Munos: Particle Filter-based Policy Gradient in POMDPs. NIPS 2008: 337-344
- [c19]Yizao Wang, Jean-Yves Audibert, Rémi Munos: Algorithms for Infinitely Many-Armed Bandits. NIPS 2008: 1729-1736
- [e1]Sertan Girgin, Manuel Loth, Rémi Munos, Philippe Preux, Daniil Ryabko (Eds.): Recent Advances in Reinforcement Learning, 8th European Workshop, EWRL 2008, Villeneuve d'Ascq, France, June 30 - July 3, 2008, Revised and Selected Papers. Lecture Notes in Computer Science 5323, Springer 2008, ISBN 978-3-540-89721-7
- [i2]Sébastien Bubeck, Rémi Munos, Gilles Stoltz: Pure Exploration for Multi-Armed Bandit Problems. CoRR abs/0802.2655 (2008)
- 2007
- [j7]Rémi Munos: Analyse en norme Lp de l'algorithme d'itérations sur les valeurs avec approximations. Revue d'Intelligence Artificielle 21(1): 53-74 (2007)
- [j6]Rémi Munos: Performance Bounds in Lp-norm for Approximate Value Iteration. SIAM J. Control and Optimization 46(2): 541-561 (2007)
- [c18]Jean-Yves Audibert, Rémi Munos, Csaba Szepesvári: Tuning Bandit Algorithms in Stochastic Environments. ALT 2007: 150-165
- [c17]András Antos, Rémi Munos, Csaba Szepesvári: Fitted Q-iteration in continuous action-space MDPs. NIPS 2007
- [c16]
- [i1]
- 2006
- [j5]Rémi Munos: Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation. Journal of Machine Learning Research 7: 413-427 (2006)
- [j4]Rémi Munos: Policy Gradient in Continuous Time. Journal of Machine Learning Research 7: 771-791 (2006)
- [c15]András Antos, Csaba Szepesvári, Rémi Munos: Learning Near-Optimal Policies with Bellman-Residual Minimization Based Fitted Policy Iteration and a Single Sample Path. COLT 2006: 574-588
- 2005
- [j3]Emmanuel Gobet, Rémi Munos: Sensitivity Analysis Using It[o-circumflex]--Malliavin Calculus and Martingales, and Application to Stochastic Optimal Control. SIAM J. Control and Optimization 43(5): 1676-1713 (2005)
- [c14]
- [c13]Rémi Munos: Geometric Variance Reduction in Markov Chains. Application to Value Function and Gradient Estimation. AAAI 2005: 1012-1017
- [c12]
- [c11]Csaba Szepesvári, Rémi Munos: Finite time bounds for sampling based fitted value iteration. ICML 2005: 880-887
- 2003
- [c10]
- 2002
- [j2]Rémi Munos, Andrew W. Moore: Variable Resolution Discretization in Optimal Control. Machine Learning 49(2-3): 291-323 (2002)
- 2001
- [c9]
- 2000
- [j1]Rémi Munos: A Study of Reinforcement Learning in the Continuous Case by the Means of Viscosity Solutions. Machine Learning 40(3): 265-299 (2000)
- [c8]Rémi Munos, Andrew W. Moore: Rates of Convergence for Variable Resolution Schemes in Optimal Control. ICML 2000: 647-654
1990 – 1999
- 1999
- [c7]Rémi Munos, Andrew W. Moore: Variable Resolution Discretization for High-Accuracy Solutions of Optimal Control Problems. IJCAI 1999: 1348-1355
- 1998
- [c6]Rémi Munos: A General Convergence Method for Reinforcement Learning in the Continuous Case. ECML 1998: 394-405
- [c5]Rémi Munos, Andrew W. Moore: Barycentric Interpolators for Continuous Space and Time Reinforcement Learning. NIPS 1998: 1024-1030
- 1997
- [c4]Rémi Munos: Finite-Element Methods with Local Triangulation Refinement for Continuous Reimforcement Learning Problems. ECML 1997: 170-182
- [c3]Rémi Munos: A Convergent Reinforcement Learning Algorithm in the Continuous Case Based on a Finite Difference Method. IJCAI (2) 1997: 826-831
- [c2]Rémi Munos, Paul Bourgine: Reinforcement Learning for Continuous Stochastic Control Problems. NIPS 1997
- 1996
- [c1]Rémi Munos: A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning. ICML 1996: 337-345
Coauthor Index
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last updated on 2013-05-28 21:40 CEST by the dblp team