3 papers
math.ST2026
Handling Covariate Mismatch in Federated Linear Prediction
Alexis Ayme, Rémi Khellaf
Federated learning enables institutions to train predictive models collaboratively without sharing raw data, addressing privacy and regulatory constraints. In the standard horizont…
stat.ML2025
Breaking the curse of dimensionality for linear rules: optimal predictors over the ellipsoid
Alexis Ayme, Bruno Loureiro
In this work, we address the following question: What minimal structural assumptions are needed to prevent the degradation of statistical learning bounds with increasing dimensiona…
math.ST2024
A primer on linear classification with missing data
Angel D Reyero Lobo, Alexis Ayme, Claire Boyer +1
Supervised learning with missing data aims at building the best prediction of a target output based on partially-observed inputs. Major approaches to address this problem can be de…