17 citations · 34 across the 6 of their papers we have counts for
8 papers
Sequential causal inference in a single world of connected units
Aurelien Bibaut, Maya Petersen, Nikos Vlassis +2
We consider adaptive designs for a trial involving N individuals that we follow along T time steps. We allow for the variables of one individual to depend on its past and on the pa…
Sufficient and insufficient conditions for the stochastic convergence of Cesàro means
Aurélien F. Bibaut, Alex Luedtke, Mark J. van der Laan
We study the stochastic convergence of the Cesàro mean of a sequence of random variables. These arise naturally in statistical problems that have a sequential component, where the…
Rate-adaptive model selection over a collection of black-box contextual bandit algorithms
Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
We consider the model selection task in the stochastic contextual bandit setting. Suppose we are given a collection of base contextual bandit algorithms. We provide a master algori…
Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits
Aurélien F. Bibaut, Antoine Chambaz, Mark J. van der Laan
We propose the Generalized Policy Elimination (GPE) algorithm, an oracle-efficient contextual bandit (CB) algorithm inspired by the Policy Elimination algorithm of \cite{dudik2011}…
More Efficient Off-Policy Evaluation through Regularized Targeted Learning
Aurélien F. Bibaut, Ivana Malenica, Nikos Vlassis +1
We study the problem of off-policy evaluation (OPE) in Reinforcement Learning (RL), where the aim is to estimate the performance of a new policy given historical data that may have…
Fast rates for empirical risk minimization over càdlàg functions with bounded sectional variation norm
Aurélien F. Bibaut, Mark J. van der Laan
Empirical risk minimization over classes functions that are bounded for some version of the variation norm has a long history, starting with Total Variation Denoising (Rudin et al.…