6 papers
JEPAMatch: Geometric Representation Shaping for Semi-Supervised Learning
Ali Aghababaei-Harandi, Aude Sportisse, Massih-Reza Amini
Semi-supervised learning has emerged as a powerful paradigm for leveraging large amounts of unlabeled data to improve the performance of machine learning models when labeled data a…
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…
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…
Debiasing Stochastic Gradient Descent to handle missing values
Julie Josse, Aude Sportisse, Claire Boyer +1
Stochastic gradient algorithm is a key ingredient of many machine learning methods, particularly appropriate for large-scale learning.However, a major caveat of large data is their…
Estimation and imputation in Probabilistic Principal Component Analysis with Missing Not At Random data
Aude Sportisse, Claire Boyer, Julie Josse
Missing Not At Random (MNAR) values lead to significant biases in the data, since the probability of missingness depends on the unobserved values.They are ''not ignorable'' in the…
Imputation and low-rank estimation with Missing Not At Random data
Aude Sportisse, Claire Boyer, Julie Josse
Missing values challenge data analysis because many supervised and unsupervised learning methods cannot be applied directly to incomplete data. Matrix completion based on low-rank…