papers

Publications (10)

math.ST2021

Testing exchangeability: fork-convexity, supermartingales, and e-processes

Aaditya Ramdas, Johannes Ruf, Martin Larsson +1

Suppose we observe an infinite series of coin flips , and wish to sequentially test the null that these binary random variables are exchangeable. Nonnegative superm…

stat.ML2021

A/B/n Testing with Control in the Presence of Subpopulations

Yoan Russac, Christina Katsimerou, Dennis Bohle +3

Motivated by A/B/n testing applications, we consider a finite set of distributions (called \emph{arms}), one of which is treated as a \emph{control}. We assume that the population…

stat.ML2018

Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling

Emilie Kaufmann, Wouter Koolen, Aurelien Garivier

Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning…

stat.ML2017

Monte-Carlo Tree Search by Best Arm Identification

Emilie Kaufmann, Wouter Koolen

Recent advances in bandit tools and techniques for sequential learning are steadily enabling new applications and are promising the resolution of a range of challenging related pro…

stat.ML2021

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals

Emilie Kaufmann, Wouter Koolen

This paper presents new deviation inequalities that are valid uniformly in time under adaptive sampling in a multi-armed bandit model. The deviations are measured using the Kullbac…

stat.ML2025

On the Robustness of Kernel Ridge Regression Using the Cauchy Loss Function

Hongwei Wen, Annika Betken, Wouter Koolen

Robust regression aims to develop methods for estimating an unknown regression function in the presence of outliers, heavy-tailed distributions, or contaminated data, which can sev…