On Bootstrapping the ROC Curve. - Patrice Bertail; Stéphan Clémençon; Nicolas Vayatis (NIPS, 2008)

Approximate regenerative-block bootstrap for Markov chains. - Patrice Bertail; Stéphan Clémençon (Comput. Stat. Data Anal., 2008)

Scaling up M-estimation via sampling designs: The Horvitz-Thompson stochastic gradient descent. - Stéphan Clémençon; Patrice Bertail; Emilie Chautru (IEEE BigData, 2014)

Learning from Survey Training Samples: Rate Bounds for Horvitz-Thompson Risk Minimizers. - Stéphan Clémençon; Patrice Bertail; Guillaume Papa (ACML, 2016)

Generalization Bounds for Minimum Volume Set Estimation based on Markovian Data. - Patrice Bertail; Gabriela Ciolek; Stéphan Clémençon (ISAIM, 2018)

On Medians of (Randomized) Pairwise Means. - Stéphan Clémençon; Pierre Laforgue; Patrice Bertail (ICML, 2019)

Statistical learning based on Markovian data maximal deviation inequalities and learning rates. - Stéphan Clémençon; Patrice Bertail; Gabriela Ciolek (Ann. Math. Artif. Intell., 2020)

Learning from Biased Data: A Semi-Parametric Approach. - Patrice Bertail; Stéphan Clémençon; Yannick Guyonvarch; Nathan Noiry (ICML, 2021)

Exponential bounds for regularized Hotelling's T2 statistic in high dimension. - El Mehdi Issouani; Patrice Bertail; Emmanuelle Gautherat (J. Multivar. Anal., 2024)