777 citations · 8.1k across the 109 of their papers we have counts for
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On double-descent in uncertainty quantification in overparametrized models
Lucas Clarté, Bruno Loureiro, Florent Krzakala +1
Uncertainty quantification is a central challenge in reliable and trustworthy machine learning. Naive measures such as last-layer scores are well-known to yield overconfident estim…
Thresholds of descending algorithms in inference problems
Stefano Sarao Mannelli, Lenka Zdeborova
We review recent works on analyzing the dynamics of gradient-based algorithms in a prototypical statistical inference problem. Using methods and insights from the physics of glassy…
Who is Afraid of Big Bad Minima? Analysis of Gradient-Flow in a Spiked Matrix-Tensor Model
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota +2
Gradient-based algorithms are effective for many machine learning tasks, but despite ample recent effort and some progress, it often remains unclear why they work in practice in op…
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models
Stefano Sarao Mannelli, Florent Krzakala, Pierfrancesco Urbani +1
In this work we analyse quantitatively the interplay between the loss landscape and performance of descent algorithms in a prototypical inference problem, the spiked matrix-tensor…
Marvels and Pitfalls of the Langevin Algorithm in Noisy High-dimensional Inference
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota +3
Gradient-descent-based algorithms and their stochastic versions have widespread applications in machine learning and statistical inference. In this work we perform an analytic stud…
The committee machine: Computational to statistical gaps in learning a two-layers neural network
Benjamin Aubin, Antoine Maillard, Jean Barbier +3
Heuristic tools from statistical physics have been used in the past to locate the phase transitions and compute the optimal learning and generalization errors in the teacher-studen…