Search Results for author: Antoine Chambaz

Found 9 papers, 2 papers with code

Positivity-free Policy Learning with Observational Data

1 code implementation10 Oct 2023 Pan Zhao, Antoine Chambaz, Julie Josse, Shu Yang

Policy learning utilizing observational data is pivotal across various domains, with the objective of learning the optimal treatment assignment policy while adhering to specific constraints such as fairness, budget, and simplicity.

Fairness

Personalized Online Machine Learning

no code implementations21 Sep 2021 Ivana Malenica, Rachael V. Phillips, Romain Pirracchio, Antoine Chambaz, Alan Hubbard, Mark J. Van Der Laan

In this work, we introduce the Personalized Online Super Learner (POSL) -- an online ensembling algorithm for streaming data whose optimization procedure accommodates varying degrees of personalization.

BIG-bench Machine Learning Time Series +1

Optimal transport-based machine learning to match specific patterns: application to the detection of molecular regulation patterns in omics data

1 code implementation21 Jul 2021 Thi Thanh Yen Nguyen, Warith Harchaoui, Lucile Mégret, Cloe Mendoza, Olivier Bouaziz, Christian Neri, Antoine Chambaz

We present several algorithms designed to learn a pattern of correspondence between two data sets in situations where it is desirable to match elements that exhibit a relationship belonging to a known parametric model.

BIG-bench Machine Learning

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning

no code implementations NeurIPS 2021 Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark van der Laan

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 guarantees can fail when we use adaptively collected data, such as the result of running a contextual bandit algorithm.

regression

Post-Contextual-Bandit Inference

no code implementations NeurIPS 2021 Aurélien Bibaut, Antoine Chambaz, Maria Dimakopoulou, Nathan Kallus, Mark van der Laan

The adaptive nature of the data collected by contextual bandit algorithms, however, makes this difficult: standard estimators are no longer asymptotically normally distributed and classic confidence intervals fail to provide correct coverage.

valid

Rate-adaptive model selection over a collection of black-box contextual bandit algorithms

no code implementations5 Jun 2020 Aurélien F. Bibaut, Antoine Chambaz, Mark J. Van Der Laan

To the best of our knowledge, our proposal is the first one to be rate-adaptive for a collection of general black-box contextual bandit algorithms: it achieves the same regret rate as the best candidate.

Model Selection

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits

no code implementations5 Mar 2020 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}.

Multi-Armed Bandits

Collaborative targeted inference from continuously indexed nuisance parameter estimators

no code implementations31 Mar 2018 Cheng Ju, Antoine Chambaz, Mark J. Van Der Laan

Say that the above product is not fast enough and the algorithm for the $G$-component is fine-tuned by a real-valued $h$.

Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits

no code implementations30 Jun 2016 Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz

We study a generalization of the multi-armed bandit problem with multiple plays where there is a cost associated with pulling each arm and the agent has a budget at each time that dictates how much she can expect to spend.

Thompson Sampling

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