4 citations · 4 across the 4 of their papers we have counts for
4 papers
Maximum-Volume Nonnegative Matrix Factorization
Olivier Vu Thanh, Nicolas Gillis
Nonnegative matrix factorization (NMF) is a popular data embedding technique. Given a nonnegative data matrix , it aims at finding two lower dimensional matrices, and , s…
From latent factors to language: a user study on LLM-generated explanations for an inherently interpretable matrix-based recommender system
Maxime Manderlier, Fabian Lecron, Olivier Vu Thanh +1
We investigate whether large language models (LLMs) can generate effective, user-facing explanations from a mathematically interpretable recommendation model. The model is based on…
Low-Rank Matrix Factorizations with Volume-based Constraints and Regularizations
Olivier Vu Thanh
Low-rank matrix factorizations are a class of linear models widely used in various fields such as machine learning, signal processing, and data analysis. These models approximate a…
Bounded Simplex-Structured Matrix Factorization: Algorithms, Identifiability and Applications
Olivier Vu Thanh, Nicolas Gillis, Fabian Lecron
In this paper, we propose a new low-rank matrix factorization model dubbed bounded simplex-structured matrix factorization (BSSMF). Given an input matrix and a factorization ra…