A nonasymptotic law of iterated logarithm for robust online estimators. - Victor-Emmanuel Brunel; Arnak S. Dalalyan; Nicolas Schreuder (CoRR, 2019)

A nonasymptotic law of iterated logarithm for general M-estimators. - Arnak S. Dalalyan; Nicolas Schreuder; Victor-Emmanuel Brunel (AISTATS, 2020)

An example of prediction which complies with Demographic Parity and equalizes group-wise risks in the context of regression. - Evgenii Chzhen; Nicolas Schreuder (CoRR, 2020)

Statistical guarantees for generative models without domination. - Nicolas Schreuder; Victor-Emmanuel Brunel; Arnak S. Dalalyan (ALT, 2021)

Classification with abstention but without disparities. - Nicolas Schreuder; Evgenii Chzhen (UAI, 2021)

Nyström Kernel Mean Embeddings. - Antoine Chatalic; Nicolas Schreuder; Lorenzo Rosasco; Alessandro Rudi (ICML, 2022)

Fair learning with Wasserstein barycenters for non-decomposable performance measures. - Solenne Gaucher; Nicolas Schreuder; Evgenii Chzhen (AISTATS, 2023)

Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling. - Antoine Chatalic; Nicolas Schreuder; Ernesto De Vito; Lorenzo Rosasco (CoRR, 2023)

Addressing Bias in Online Selection with Limited Budget of Comparisons. - Ziyad Benomar; Evgenii Chzhen; Nicolas Schreuder; Vianney Perchet (NeurIPS, 2024)

The late-stage training dynamics of (stochastic) subgradient descent on homogeneous neural networks. - Sholom Schechtman; Nicolas Schreuder (COLT, 2025)

An Efficient Permutation-Based Kernel Two-Sample Test. - Antoine Chatalic; Marco Letizia; Nicolas Schreuder; Lorenzo Rosasco (CoRR, 2025)