45 citations · 151 across the 18 of their papers we have counts for
4 papers · 1 filter
Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
Jasraj Singh, Shelvia Wongso, Jeremie Houssineau +1
Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formulation depends on expectations and dive…
Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching
Bastien Dussap, Gilles Blanchard, Badr-Eddine Chérief-Abdellatif
Quantification learning deals with the task of estimating the target label distribution under label shift. In this paper, we first present a unifying framework, distribution featur…
Bayes meets Bernstein at the Meta Level: an Analysis of Fast Rates in Meta-Learning with PAC-Bayes
Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif
Bernstein's condition is a key assumption that guarantees fast rates in machine learning. For example, the Gibbs algorithm with prior has an excess risk in , as oppos…
A Generalization Bound for Online Variational Inference
Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversa…