### Abstract

We consider a stochastic linear bandit model in which the available actions correspond to arbitrary context vectors whose associated rewards follow a non-stationary linear regression model. In this setting, the unknown regression parameter is allowed to vary in time. To address this problem, we propose `DLinUCB`

, a novel optimistic algorithm based on discounted linear regression, where exponential weights are used to smoothly forget the past. This involves studying the deviations of the sequential weighted least-squares estimator under generic assumptions. As a by-product, we obtain novel deviation results that can be used beyond non-stationary environments. We provide theoretical guarantees on the behavior of `DLinUCB`

in both slowly-varying and abruptly-changing environments. We obtain an upper bound on the dynamic regret that is of order $d^{\frac{2}{3}} B_T^{\frac{1}{3}} T^{\frac{2}{3}}$, where $B_T$ is a measure of non-stationarity ($d$ and $T$ being, respectively, dimension and horizon). This rate is known to be optimal. We also illustrate the empirical performance of `DLinUCB`

and compare it with recently proposed alternatives in simulated environments.

Publication

In *Proceedings of NeurIPS2019*

###### PhD Student in Machine Learning

My research interests include sequential learning, bandits theory and machine learning.