Thompson Sampling for Combinatorial Bandits and its Application to Online Feature Selection

In this work, we address the combinatorial optimization problem in the stochastic bandit setting with bandit feedback. We propose to use the seminal Thompson Sampling algorithm under an assumption on rewards expectations. More specif-ically, we tackle the online feature selection problem where results show that Thompson Sampling performs well. Additionnally, we discuss the challenges associated with online feature selection and highlight relevant future work directions