2 papers
math.OC2025
Oblivious Stochastic Composite Optimization
Clément Lezane, Alexandre d'Aspremont
In stochastic convex optimization problems, most existing adaptive methods rely on prior knowledge about the diameter bound when the smoothness or the Lipschitz constant is unk…
cs.LG2025
Naive Feature Selection: a Nearly Tight Convex Relaxation for Sparse Naive Bayes
Armin Askari, Alexandre d'Aspremont, Laurent El Ghaoui
Due to its linear complexity, naive Bayes classification remains an attractive supervised learning method, especially in very large-scale settings. We propose a sparse version of n…