1 code implementation • 12 Oct 2022 • Cedric Gerbelot, Emanuele Troiani, Francesca Mignacco, Florent Krzakala, Lenka Zdeborova
We prove closed-form equations for the exact high-dimensional asymptotics of a family of first order gradient-based methods, learning an estimator (e. g. M-estimator, shallow neural network, ...) from observations on Gaussian data with empirical risk minimization.
no code implementations • 2 Jan 2020 • Stefano Sarao Mannelli, Lenka Zdeborova
We review recent works on analyzing the dynamics of gradient-based algorithms in a prototypical statistical inference problem.
no code implementations • NeurIPS Workshop Deep_Invers 2019 • Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborova
We consider the problem of compressed sensing and of (real-valued) phase retrieval with random measurement matrix.
no code implementations • NeurIPS 2016 • Jean Barbier, Mohamad Dia, Nicolas Macris, Florent Krzakala, Thibault Lesieur, Lenka Zdeborova
We also show that for a large set of parameters, an iterative algorithm called approximate message-passing is Bayes optimal.
1 code implementation • 1 Mar 2015 • Thibault Lesieur, Florent Krzakala, Lenka Zdeborova
We study optimal estimation for sparse principal component analysis when the number of non-zero elements is small but on the same order as the dimension of the data.
no code implementations • 17 Jul 2012 • Xiaoran Yan, Cosma Rohilla Shalizi, Jacob E. Jensen, Florent Krzakala, Cristopher Moore, Lenka Zdeborova, Pan Zhang, Yaojia Zhu
We present the first principled and tractable approach to model selection between standard and degree-corrected block models, based on new large-graph asymptotics for the distribution of log-likelihood ratios under the stochastic block model, finding substantial departures from classical results for sparse graphs.