9 citations · 39 across the 17 of their papers we have counts for
7 papers · 1 filter
Learning with Hidden Factorial Structure
Charles Arnal, Clement Berenfeld, Simon Rosenberg +1
Statistical learning in high-dimensional spaces is challenging without a strong underlying data structure. Recent advances with foundational models suggest that text and image data…
Birth of a Transformer: A Memory Viewpoint
Alberto Bietti, Vivien Cabannes, Diane Bouchacourt +2
Large language models based on transformers have achieved great empirical successes. However, as they are deployed more widely, there is a growing need to better understand their i…
How many samples are needed to leverage smoothness?
Vivien Cabannes, Stefano Vigogna
A core principle in statistical learning is that smoothness of target functions allows to break the curse of dimensionality. However, learning a smooth function seems to require en…
The SSL Interplay: Augmentations, Inductive Bias, and Generalization
Vivien Cabannes, Bobak T. Kiani, Randall Balestriero +2
Self-supervised learning (SSL) has emerged as a powerful framework to learn representations from raw data without supervision. Yet in practice, engineers face issues such as instab…
A Case of Exponential Convergence Rates for SVM
Vivien Cabannes, Stefano Vigogna
Classification is often the first problem described in introductory machine learning classes. Generalization guarantees of classification have historically been offered by Vapnik-C…
Fast rates in structured prediction
Vivien Cabannes, Alessandro Rudi, Francis Bach
Discrete supervised learning problems such as classification are often tackled by introducing a continuous surrogate problem akin to regression. Bounding the original error, betwee…
Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning
Vivien Cabannes, Loucas Pillaud-Vivien, Francis Bach +1
As annotations of data can be scarce in large-scale practical problems, leveraging unlabelled examples is one of the most important aspects of machine learning. This is the aim of…