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Publications

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An adaptive and efficient multi-goal exploration
Jean Tarbouriech *, Omar Darwiche Domingues *, Pierre Ménard *, Matteo Pirotta *, Michal Valko, Alessandro Lazaric *
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Marginalized operators for off-policy reinforcement learning
Yunhao Tang *, Mark Rowland, Remi Munos, Michal Valko
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Hierarchical Bayesian Bandits
Manzil Zaheer, Mohammad Ghavamzadeh *, Joey Hong *, Branislav Kveton *
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Double Control Variates for Gradient Estimation in Discrete Latent Variable Models
Michalis Titsias, Jiaxin Shi *
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Confident Least Square Value Iteration with Local Access to a Simulator
Botao Hao, Nevena Lazic, Dong Yin, Yasin Abbasi Yadkori, Csaba Szepesvari
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A Functional Mirror Ascent View of Policy Gradient Methods with Function Approximation
Sharan Vaswani *, Olivier Bachem *, Simone Totaro *, Robert Müller *, M Geist *, Marlos C. Machado, P Castro *, Nicolas Le Roux *
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On The Effect of Auxiliary Tasks on Representation Dynamics
Clare Lyle *, Mark Rowland, Georg Ostrovski, Will Dabney
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Confident Off-Policy Evaluation and Selection through Self-Normalized Importance Weighting
Ilja Kuzborskij, Claire Vernade, Andras Gyorgy, Csaba Szepesvari
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A kernel-based approach to non-stationary reinforcement learning in metric spaces
O Domingues *, Pierre Ménard *, Matteo Pirotta *, Emilie Kaufmann *, Michal Valko
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Fixed-Confidence Guarantees for Bayesian Best-Arm Identification
X Shang *, R de Heide *, Emilie Kaufmann *, Pierre Ménard *, Michal Valko
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A single algorithm for both restless and rested rotting bandits
Julien Seznec *, Pierre Ménard *, Alessandro Lazaric *, Michal Valko
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Non-exchangeable feature allocation models with sublinear growth of the feature sizes
G Di Benedetto *, F Caron *, Yee Whye Teh
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A Distributional Analysis of Sampling-Based Reinforcement Learning Algorithms
Philip Amortila *, Doina Precup, Prakash Panangaden *, Marc Bellemare *
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Learning Nash Equilibrium for General-Sum Markov Games from Batch Data
Julien Perolat, Florian Strub *, Bilal Piot, Olivier Pietquin *
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Toward Minimax Off-policy Value Estimation
L Li *, Rémi Munos, David Szepesvpari
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