We present a unified technique for sequential estimation of convex divergences between
distributions, including integral probability metrics like the kernel maximum mean …

In bandit multiple hypothesis testing, each arm corresponds to a different null hypothesis that
we wish to test, and the goal is to design adaptive algorithms that correctly identify large set …

We introduce a new setting, optimize-and-estimate structured bandits. Here, a policy must
select a batch of arms, each characterized by its own context, that would allow it to both …

Estimation and inference in statistics pose significant challenges when data are collected
adaptively. Even in linear models, the Ordinary Least Squares (OLS) estimator may fail to …

An emerging dilemma that faces practitioners in large scale online experimentation for e-
commerce is whether to use Multi-Armed Bandit (MAB) algorithms for testing or traditional …

Entropy regularization is known to improve exploration in sequential decision-making
problems. We show that this same mechanism can also lead to nearly unbiased and lower …

We propose a novel nonparametric sequential test for composite hypotheses for means of
multiple data streams. Our proposed method,\emph {peeking with expectation-based …

Given n experiment subjects with potentially heterogeneous covariates and two possible
treatments, namely active treatment and control, this paper addresses the fundamental …

We explore the promises and challenges of employing sequential decision-making
algorithms--such as bandits, reinforcement learning, and active learning--in law and public …

Optimal transport is a flexible framework for comparing probability distributions, which has
received a recent surge of interest as a methodological tool in statistics. The aim of this …