The topic of dynamic pricing and learning has received a considerable amount of attention
in recent years, from different scientific communities. We survey these literature streams: we …

Thompson Sampling (TS) is one of the most effective algorithms for solving contextual multi-
armed bandit problems. In this paper, we propose a new algorithm, called Neural Thompson …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

We study the stochastic contextual bandit problem, where the reward is generated from an
unknown function with additive noise. No assumption is made about the reward function …

We introduce a new, efficient, principled and backpropagation-compatible algorithm for
learning a probability distribution on the weights of a neural network, called Bayes by …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

Contextual bandits are widely used in Internet services from news recommendation to
advertising, and to Web search. Generalized linear models (logistical regression in …

Thompson Sampling is one of the oldest heuristics for multi-armed bandit problems. It is a
randomized algorithm based on Bayesian ideas, and has recently generated significant …

This paper considers the use of a simple posterior sampling algorithm to balance between
exploration and exploitation when learning to optimize actions such as in multiarmed bandit …