6 papers
Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging
Nawel Arab, Mohammed Nabil El Korso, Isabelle Vin +1
State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture heavy-tailed or outlier-prone mea…
Robust Expectation-Maximization for Covariance Estimation in SIRV Models with Missing Data: Application to InSAR Time Series
M. Cherifi, M. N. El Korso, A. Hippert-Ferrer +1
This paper presents a robust Expectation-Maximization framework for covariance estimation in Scale-Invariant Random Vector (SIRV) models with missing data under ignorable missingne…
Amortized Variational Inference for Logistic Regression with Missing Covariates
M. Cherifi, Aude Sportisse, Xujia Zhu +2
Missing covariate data pose a significant challenge to statistical inference and machine learning, particularly for classification tasks like logistic regression. Classical iterati…
Radar Detection through Rectified Flow Matching
P. Meena, Y. A. Rouzoumka, J. Pinsolle +3
Radar target detection in the presence of a mixture of non-Gaussian clutter and white thermal noise is a challenging problem. This paper proposes a Rectified Flow Matching-based me…
Missing Data in Signal Processing and Machine Learning: Models, Methods and Modern Approaches
Alexandre Hippert-Ferrer, Aude Sportisse, Amirhossein Javaheri +2
This tutorial aims to provide signal processing (SP) and machine learning (ML) practitioners with vital tools, in an accessible way, to answer the question: How to deal with missin…
Maximum Likelihood for Logistic Regression Model with Incomplete and Hybrid-Type Covariates
Mohamed Cherifi, Xujia Zhu, Mohammed Nabil El Korso +1
Logistic regression is a fundamental and widely used statistical method for modeling binary outcomes based on covariates. However, the presence of missing data, particularly in set…