Sifting abstracts from Medline and evaluating their relevance to molecular biology. - Natalia Grabar; Marie-Christine Jaulent; Antoine Chambaz; Céline Lefebvre; Christian Néri (MIE, 2006)

tmle.npvi: targeted, integrative search of associations between DNA copy number and gene expression, accounting for DNA methylation. - Antoine Chambaz; Pierre Neuvial (Bioinform., 2015)

Asymptotically Optimal Algorithms for Multiple Play Bandits with Partial Feedback. - Alexander Luedtke; Emilie Kaufmann; Antoine Chambaz (CoRR, 2016)

Asymptotically optimal algorithms for budgeted multiple play bandits. - Alexander Luedtke; Emilie Kaufmann; Antoine Chambaz (Mach. Learn., 2019)

Generalized Policy Elimination: an efficient algorithm for Nonparametric Contextual Bandits. - Aurélien Bibaut; Antoine Chambaz; Mark J. van der Laan (UAI, 2020)

Rate-adaptive model selection over a collection of black-box contextual bandit algorithms. - Aurélien F. Bibaut; Antoine Chambaz; Mark J. van der Laan (CoRR, 2020)

Targeted Machine Learning: how we can use machine learning for causal inference. - Antoine Chambaz (EGC, 2021)

Post-Contextual-Bandit Inference. - Aurélien Bibaut; Maria Dimakopoulou; Nathan Kallus; Antoine Chambaz; Mark J. van der Laan (NeurIPS, 2021)

Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning. - Aurélien Bibaut; Nathan Kallus; Maria Dimakopoulou; Antoine Chambaz; Mark J. van der Laan (NeurIPS, 2021)

Personalized Online Machine Learning. - Ivana Malenica; Rachael V. Phillips; Romain Pirracchio; Antoine Chambaz; Alan E. Hubbard; Mark J. van der Laan (CoRR, 2021)

A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models. - Alexander G. Reisach; Myriam Tami; Christof Seiler; Antoine Chambaz; Sebastian Weichwald (NeurIPS, 2023)

Simple Sorting Criteria Help Find the Causal Order in Additive Noise Models. - Alexander G. Reisach; Myriam Tami; Christof Seiler; Antoine Chambaz; Sebastian Weichwald (CoRR, 2023)

Positivity-free Policy Learning with Observational Data. - Pan Zhao; Antoine Chambaz; Julie Josse; Shu Yang (AISTATS, 2024)

Quantile Super Learning for independent and online settings with application to solar power forecasting. - Herbert Susmann; Antoine Chambaz (Comput. Stat. Data Anal., 2025)

Probabilistic prediction of arrivals and hospitalizations in emergency departments in Île-de-France. - Herbert Susmann; Antoine Chambaz; Julie Josse; Philippe Aegerter; Mathias Wargon; Emmanuel Bacry (Int. J. Medical Informatics, 2025)

Forecasting the cost of drought events in France by Super Learning from a short time series of many slightly dependent data. - Geoffrey Ecoto; Aurélien F. Bibaut; Antoine Chambaz (Comput. Stat., 2025)