no code implementations • 23 May 2024 • Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif, Yevgeny Seldin
While PAC-Bayes allows construction of data-informed priors, the final confidence intervals depend only on the number of points that were not used for the construction of the prior, whereas confidence information in the prior, which is related to the number of points used to construct the prior, is lost.
no code implementations • 7 Jun 2023 • Bastien Dussap, Gilles Blanchard, Badr-Eddine Chérief-Abdellatif
Quantification learning deals with the task of estimating the target label distribution under label shift.
no code implementations • 23 Feb 2023 • Charles Riou, Pierre Alquier, Badr-Eddine Chérief-Abdellatif
Bernstein's condition is a key assumption that guarantees fast rates in machine learning.
no code implementations • 23 Feb 2022 • Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet, Benjamin Guedj
Despite its wide use and empirical successes, the theoretical understanding and study of the behaviour and performance of the variational autoencoder (VAE) have only emerged in the past few years.
no code implementations • 12 Dec 2019 • Badr-Eddine Chérief-Abdellatif, Pierre Alquier
Many works in statistics aim at designing a universal estimation procedure, that is, an estimator that would converge to the best approximation of the (unknown) data generating distribution in a model, without any assumption on this distribution.
no code implementations • pproximateinference AABI Symposium 2019 • Badr-Eddine Chérief-Abdellatif, Pierre Alquier
In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates.
no code implementations • ICML 2020 • Badr-Eddine Chérief-Abdellatif
Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning.
no code implementations • 8 Apr 2019 • Badr-Eddine Chérief-Abdellatif, Pierre Alquier, Mohammad Emtiyaz Khan
Our work in this paper presents theoretical justifications in favor of online algorithms relying on approximate Bayesian methods.