Travelled to:1 × China
1 × France
1 × Germany
1 × Israel
1 × Italy
1 × United Kingdom
4 × USA
Collaborated with:∅ M.Ghavamzadeh A.Lazaric M.Valko A.Carpentier C.Szepesvári A.W.Moore M.G.Azar B.Kappen M.K.Hanawal V.Saligrama B.Kveton T.Kocák M.Zoghi S.Whiteson M.d.Rijke M.W.Hoffman
Talks about:sampl (4) finit (4) reinforc (3) analysi (3) bandit (3) optim (3) learn (3) bound (3) iter (3) algorithm (2)
Person: Rémi Munos
DBLP: Munos:R=eacute=mi
Contributed to:
2015201420132012201120102005200320001996Wrote 13 papers:
- ICML-2015-HanawalSVM
- Cheap Bandits (MKH, VS, MV, RM), pp. 2133–2142.
- ICML-c2-2014-ValkoMKK #graph
- Spectral Bandits for Smooth Graph Functions (MV, RM, BK, TK), pp. 46–54.
- ICML-c2-2014-ZoghiWMR #bound #problem
- Relative Upper Confidence Bound for the K-Armed Dueling Bandit Problem (MZ, SW, RM, MdR), pp. 10–18.
- ICML-c2-2013-CarpentierM #integration #monte carlo #towards
- Toward Optimal Stratification for Stratified Monte-Carlo Integration (AC, RM), pp. 28–36.
- ICML-c2-2013-ValkoCM #optimisation #probability
- Stochastic Simultaneous Optimistic Optimization (MV, AC, RM), pp. 19–27.
- ICML-2012-AzarMK #complexity #generative #learning #on the
- On the Sample Complexity of Reinforcement Learning with a Generative Model (MGA, RM, BK), p. 222.
- ICML-2011-GhavamzadehLMH #analysis
- Finite-Sample Analysis of Lasso-TD (MG, AL, RM, MWH), pp. 1177–1184.
- ICML-2010-LazaricGM #algorithm #analysis #classification #policy
- Analysis of a Classification-based Policy Iteration Algorithm (AL, MG, RM), pp. 607–614.
- ICML-2010-LazaricGM10a #analysis
- Finite-Sample Analysis of LSTD (AL, MG, RM), pp. 615–622.
- ICML-2005-SzepesvariM #bound #finite
- Finite time bounds for sampling based fitted value iteration (CS, RM), pp. 880–887.
- ICML-2003-Munos #approximate #bound #fault #policy
- Error Bounds for Approximate Policy Iteration (RM), pp. 560–567.
- ICML-2000-MunosM #convergence
- Rates of Convergence for Variable Resolution Schemes in Optimal Control (RM, AWM), pp. 647–654.
- ICML-1996-Munos #algorithm #convergence #learning
- A Convergent Reinforcement Learning Algorithm in the Continuous Case: The Finite-Element Reinforcement Learning (RM), pp. 337–345.