Feb 23, 2020 · Abstract:Thompson sampling for multi-armed bandit problems is known to enjoy favorable performance in both theory and practice.
In this work we address this by proposing two efficient Langevin MCMC algorithms tailored to Thompson sampling. The resulting approximate Thompson Sampling ...
Abstract. Thompson sampling for multi-armed bandit prob- lems is known to enjoy favorable performance in both theory and practice. However, its wider.
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This work proposes two efficient Langevin MCMC algorithms tailored to Thompson sampling and derives novel posterior concentration bounds and MCMC ...
Jun 18, 2020 · Abstract. Thompson sampling for multi-armed bandit problems is known to enjoy favorable performance in both theory and practice.
We construct quickly converging Langevin algorithms to generate approximate samples that have accuracy guarantees, and we leverage novel posterior concentration ...
Abstract. Thompson sampling for multi-armed bandit prob- lems is known to enjoy favorable performance in both theory and practice. However, its wider.
Two Markov Chain Monte Carlo methods tailored to Thompson sampling are proposed, which take advantage of both posterior concentration and a sample reuse ...
Thompson sampling is a methodology for multi-armed bandit problems that is known to enjoy favorable performance in both theory and practice.
Jul 13, 2020 · Thompson sampling for multi-armed bandit problems is known to enjoy favorable performance in both theory and practice.