45 citations · 151 across the 18 of their papers we have counts for
18 papers
Convex Order Comparisons for Sub-Gamma Random Variables
El Mahdi Khribch, Badr-Eddine Chérief-Abdellatif
Recent work has shown that sub-Gaussian random variables are dominated in convex order by a sharp multiple of a Gaussian. We study the analogous question for the sub-Gamma class un…
From Bayes' Rule to Bayes Rules: Optimal Information Processing and Axiomatic Foundations Beyond Probability
Jeremie Houssineau, Badr-Eddine Chérief-Abdellatif
This paper develops principled updating rules for possibilistic inference, where uncertainty about a fixed parameter is represented by a possibility function, the maxitive analogue…
Asymptotics of Nonparametric Estimation under general non-monotone MAR missingness: A Bayesian Approach
Badr-Eddine Chérief-Abdellatif, Jeffrey Näf
Missing values are ubiquitous in statistical practice, with potentially detrimental consequences for any statistical analysis. As such, a wealth of methods and theoretical results…
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…
Predictively Oriented Posteriors
Yann McLatchie, Badr-Eddine Cherief-Abdellatif, David T. Frazier +1
We advocate for a new statistical principle that combines the most desirable aspects of both parameter inference and density estimation. This leads us to the predictively oriented…
Parametric MMD Estimation with Missing Values: Robustness to Missingness and Data Model Misspecification
Badr-Eddine Chérief-Abdellatif, Jeffrey Näf
In the missing data literature, the Maximum Likelihood Estimator (MLE) is celebrated for its ignorability property under missing at random (MAR) data. However, its sensitivity to m…
On the Asymptotics of Importance Weighted Variational Inference
Badr-Eddine Cherief-Abdellatif, Randal Douc, Arnaud Doucet +1
For complex latent variable models, the likelihood function is not available in closed form. In this context, a popular method to perform parameter estimation is Importance Weighte…
Recursive PAC-Bayes: A Frequentist Approach to Sequential Prior Updates with No Information Loss
Yi-Shan Wu, Yijie Zhang, Badr-Eddine Chérief-Abdellatif +1
PAC-Bayesian analysis is a frequentist framework for incorporating prior knowledge into learning. It was inspired by Bayesian learning, which allows sequential data processing and…
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…
On PAC-Bayesian reconstruction guarantees for VAEs
Badr-Eddine Chérief-Abdellatif, Yuyang Shi, Arnaud Doucet +1
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…
Estimation of copulas via Maximum Mean Discrepancy
Pierre Alquier, Badr-Eddine Chérief-Abdellatif, Alexis Derumigny +1
This paper deals with robust inference for parametric copula models. Estimation using Canonical Maximum Likelihood might be unstable, especially in the presence of outliers. We pro…
Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence
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 dis…
Convergence Rates of Variational Inference in Sparse Deep Learning
Badr-Eddine Chérief-Abdellatif
Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent w…
MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
Badr-Eddine Chérief-Abdellatif, Pierre Alquier
In some misspecified settings, the posterior distribution in Bayesian statistics may lead to inconsistent estimates. To fix this issue, it has been suggested to replace the likelih…
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…
Consistency of ELBO maximization for model selection
Badr-Eddine Chérief-Abdellatif
The Evidence Lower Bound (ELBO) is a quantity that plays a key role in variational inference. It can also be used as a criterion in model selection. However, though extremely popul…
Consistency of Variational Bayes Inference for Estimation and Model Selection in Mixtures
Badr-Eddine Chérief-Abdellatif, Pierre Alquier
Mixture models are widely used in Bayesian statistics and machine learning, in particular in computational biology, natural language processing and many other fields. Variational i…