19 citations · 100 across the 32 of their papers we have counts for
5 papers · 1 filter
One-step ahead sequential Super Learning from short times series of many slightly dependent data, and anticipating the cost of natural disasters
Geoffrey Ecoto, Aurélien Bibaut, Antoine Chambaz
Suppose that we observe a short time series where each time-t-specific data-structure consists of many slightly dependent data indexed by a and that we want to estimate a feature o…
Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Empirical risk minimization (ERM) is the workhorse of machine learning, whether for classification and regression or for off-policy policy learning, but its model-agnostic guarante…
Post-Contextual-Bandit Inference
Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou +2
Contextual bandit algorithms are increasingly replacing non-adaptive A/B tests in e-commerce, healthcare, and policymaking because they can both improve outcomes for study particip…
Adaptive Sequential Design for a Single Time-Series
Ivana Malenica, Aurelien Bibaut, Mark J. van der Laan
The current work is motivated by the need for robust statistical methods for precision medicine; as such, we address the need for statistical methods that provide actionable infere…
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…