Thompson sampling is an algorithm for online decision problems where actions are taken
sequentially in a manner that must balance between exploiting what is known to maximize …

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 …

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 …

Consider the sequential optimization of an expensive to evaluate and possibly non-convex
objective function $ f $ from noisy feedback, that can be considered as a continuum-armed …

We consider the exploration-exploitation dilemma in finite-horizon reinforcement learning
(RL). When the state space is large or continuous, traditional tabular approaches are …

We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based
Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration …

We introduce algorithms that achieve state-of-the-art dynamic regret bounds for non-
stationary linear stochastic bandit setting. It captures natural applications such as dynamic …

Thompson sampling and other Bayesian sequential decision-making algorithms are among
the most popular approaches to tackle explore/exploit trade-offs in (contextual) bandits. The …

This paper investigates the logistic bandit problem, a variant of the generalized linear bandit
model that utilizes a logistic model to depict the feedback from an action. While most existing …

We study the problem of learning shared structure across a sequence of dynamic pricing
experiments for related products. We consider a practical formulation in which the unknown …