45 citations · 151 across the 18 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…