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Sub-sampling for Efficient Non-Parametric Bandit Exploration

Dorian Baudry · Emilie Kaufmann · Odalric-Ambrym Maillard

Poster Session 2 #591

Keywords: [ Deep Learning ] [ Visualization or Exposition Techniques for Deep Networks ] [ Deep Learning ]


In this paper we propose the first multi-armed bandit algorithm based on re-sampling that achieves asymptotically optimal regret simultaneously for different families of arms (namely Bernoulli, Gaussian and Poisson distributions). Unlike Thompson Sampling which requires to specify a different prior to be optimal in each case, our proposal RB-SDA does not need any distribution-dependent tuning. RB-SDA belongs to the family of Sub-sampling Duelling Algorithms (SDA) which combines the sub-sampling idea first used by the BESA and SSMC algorithms with different sub-sampling schemes. In particular, RB-SDA uses Random Block sampling. We perform an experimental study assessing the flexibility and robustness of this promising novel approach for exploration in bandit models.

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