Lenka Zdeborová

research

∙ 08/27/2023

Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective

Recent years witnessed the development of powerful generative models bas...

0 Davide Ghio, et al. ∙

research

∙ 06/05/2023

Gibbs Sampling the Posterior of Neural Networks

In this paper, we study sampling from a posterior derived from a neural ...

0 Giovanni Piccioli, et al. ∙

research

∙ 05/18/2023

High-dimensional Asymptotics of Denoising Autoencoders

We address the problem of denoising data from a Gaussian mixture using a...

0 Hugo Cui, et al. ∙

research

∙ 03/09/2023

Statistical mechanics of the maximum-average submatrix problem

We study the maximum-average submatrix problem, in which given an N × N ...

0 Vittorio Erba, et al. ∙

research

∙ 03/05/2023

Expectation consistency for calibration of neural networks

Despite their incredible performance, it is well reported that deep neur...

0 Lucas Clarté, et al. ∙

research

∙ 02/17/2023

Universality laws for Gaussian mixtures in generalized linear models

Let (x_i, y_i)_i=1,…,n denote independent samples from a general mixture...

1 Yatin Dandi, et al. ∙

research

∙ 02/01/2023

Optimal Learning of Deep Random Networks of Extensive-width

We consider the problem of learning a target function corresponding to a...

0 Hugo Cui, et al. ∙

research

∙ 10/23/2022

A study of uncertainty quantification in overparametrized high-dimensional models

Uncertainty quantification is a central challenge in reliable and trustw...

7 Lucas Clarté, et al. ∙

research

∙ 10/15/2022

Disordered Systems Insights on Computational Hardness

In this review article, we discuss connections between the physics of di...

0 David Gamarnik, et al. ∙

research

∙ 10/12/2022

Rigorous dynamical mean field theory for stochastic gradient descent methods

We prove closed-form equations for the exact high-dimensional asymptotic...

0 Cédric Gerbelot, et al. ∙

research

∙ 09/07/2022

Planted matching problems on random hypergraphs

We consider the problem of inferring a matching hidden in a weighted ran...

0 Urte Adomaityte, et al. ∙

research

∙ 08/12/2022

The planted XY model: thermodynamics and inference

In this paper we study a fully connected planted spin glass named the pl...

0 Siyu Chen, et al. ∙

research

∙ 08/11/2022

Low-rank Matrix Estimation with Inhomogeneous Noise

We study low-rank matrix estimation for a generic inhomogeneous output c...

0 Alice Guionnet, et al. ∙

research

∙ 05/26/2022

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap

A simple model to study subspace clustering is the high-dimensional k-Ga...

2 Luca Pesce, et al. ∙

research

∙ 05/26/2022

Multi-layer State Evolution Under Random Convolutional Design

Signal recovery under generative neural network priors has emerged as a ...

0 Max Daniels, et al. ∙

research

∙ 05/26/2022

Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension

While classical in many theoretical settings, the assumption of Gaussian...

8 Federica Gerace, et al. ∙

research

∙ 03/22/2022

Learning curves for the multi-class teacher-student perceptron

One of the most classical results in high-dimensional learning theory pr...

17 Elisabetta Cornacchia, et al. ∙

research

∙ 03/15/2022

Optimal denoising of rotationally invariant rectangular matrices

In this manuscript we consider denoising of large rectangular matrices: ...

0 Emanuele Troiani, et al. ∙

research

∙ 02/21/2022

(Dis)assortative Partitions on Random Regular Graphs

We study the problem of assortative and disassortative partitions on ran...

0 Freya Behrens, et al. ∙

research

∙ 02/07/2022

Theoretical characterization of uncertainty in high-dimensional linear classification

Being able to reliably assess not only the accuracy but also the uncerta...

5 Lucas Clarté, et al. ∙

research

∙ 02/01/2022

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks

Despite the non-convex optimization landscape, over-parametrized shallow...

46 Rodrigo Veiga, et al. ∙

research

∙ 01/29/2022

Error Rates for Kernel Classification under Source and Capacity Conditions

In this manuscript, we consider the problem of kernel classification und...

7 Hugo Cui, et al. ∙

research

∙ 12/24/2021

Aligning random graphs with a sub-tree similarity message-passing algorithm

The problem of aligning Erdös-Rényi random graphs is a noisy, average-ca...

0 Giovanni Piccioli, et al. ∙

research

∙ 10/17/2021

Perturbative construction of mean-field equations in extensive-rank matrix factorization and denoising

Factorization of matrices where the rank of the two factors diverges lin...

0 Antoine Maillard, et al. ∙

research

∙ 06/09/2021

Probing transfer learning with a model of synthetic correlated datasets

Transfer learning can significantly improve the sample efficiency of neu...

0 Federica Gerace, et al. ∙

research

∙ 06/07/2021

Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions

Generalised linear models for multi-class classification problems are on...

14 Bruno Loureiro, et al. ∙

research

∙ 05/31/2021

Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime

In this manuscript we consider Kernel Ridge Regression (KRR) under the G...

3 Hugo Cui, et al. ∙

research

∙ 05/16/2021

Bayesian reconstruction of memories stored in neural networks from their connectivity

The advent of comprehensive synaptic wiring diagrams of large neural cir...

0 Sebastian Goldt, et al. ∙

research

∙ 03/08/2021

Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem

In this paper we investigate how gradient-based algorithms such as gradi...

0 Francesca Mignacco, et al. ∙

research

∙ 02/23/2021

Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed

A recent series of theoretical works showed that the dynamics of neural ...

0 Maria Refinetti, et al. ∙

research

∙ 02/16/2021

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model

Teacher-student models provide a powerful framework in which the typical...

6 Bruno Loureiro, et al. ∙

research

∙ 12/08/2020

Construction of optimal spectral methods in phase retrieval

We consider the phase retrieval problem, in which the observer wishes to...

0 Antoine Maillard, et al. ∙

research

∙ 12/01/2020

Solvable Model for Inheriting the Regularization through Knowledge Distillation

In recent years the empirical success of transfer learning with neural n...

0 Luca Saglietti, et al. ∙

research

∙ 10/26/2020

The planted k-factor problem

We consider the problem of recovering an unknown k-factor, hidden in a w...

0 Gabriele Sicuro, et al. ∙

research

∙ 09/20/2020

Epidemic mitigation by statistical inference from contact tracing data

Contact-tracing is an essential tool in order to mitigate the impact of ...

4 Antoine Baker, et al. ∙

research

∙ 06/27/2020

Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions

We study the dynamics of optimization and the generalization properties ...

7 Stefano Sarao Mannelli, et al. ∙

research

∙ 06/25/2020

The Gaussian equivalence of generative models for learning with two-layer neural networks

Understanding the impact of data structure on learning in neural network...

87 Sebastian Goldt, et al. ∙

research

∙ 06/12/2020

Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval

Despite the widespread use of gradient-based algorithms for optimizing h...

0 Stefano Sarao Mannelli, et al. ∙

research

∙ 06/11/2020

Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization

We consider a commonly studied supervised classification of a synthetic ...

9 Benjamin Aubin, et al. ∙

research

∙ 06/10/2020

Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification

We analyze in a closed form the learning dynamics of stochastic gradient...

57 Francesca Mignacco, et al. ∙

research

∙ 06/09/2020

Phase retrieval in high dimensions: Statistical and computational phase transitions

We consider the phase retrieval problem of reconstructing a n-dimensiona...

0 Antoine Maillard, et al. ∙

research

∙ 05/22/2020

Recovery thresholds in the sparse planted matching problem

We consider the statistical inference problem of recovering an unknown p...

0 Guilhem Semerjian, et al. ∙

research

∙ 04/03/2020

TRAMP: Compositional Inference with TRee Approximate Message Passing

We introduce tramp, standing for TRee Approximate Message Passing, a pyt...

19 Antoine Baker, et al. ∙

research

∙ 02/26/2020

The role of regularization in classification of high-dimensional noisy Gaussian mixture

We consider a high-dimensional mixture of two Gaussians in the noisy reg...

35 Francesca Mignacco, et al. ∙

research

∙ 02/21/2020

Generalisation error in learning with random features and the hidden manifold model

We study generalised linear regression and classification for a syntheti...

28 Federica Gerace, et al. ∙

research

∙ 01/02/2020

Thresholds of descending algorithms in inference problems

We review recent works on analyzing the dynamics of gradient-based algor...

27 Stefano Sarao Mannelli, et al. ∙

research

∙ 12/09/2019

Large deviations for the perceptron model and consequences for active learning

Active learning is a branch of machine learning that deals with problems...

0 Hugo Cui, et al. ∙

research

∙ 12/05/2019

Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning

Statistical learning theory provides bounds of the generalization gap, u...

12 Alia Abbara, et al. ∙

research

∙ 12/04/2019

Exact asymptotics for phase retrieval and compressed sensing with random generative priors

We consider the problem of compressed sensing and of (real-valued) phase...

5 Benjamin Aubin, et al. ∙

research

∙ 09/25/2019

Modelling the influence of data structure on learning in neural networks

The lack of crisp mathematical models that capture the structure of real...

9 Sebastian Goldt, et al. ∙

Lenka Zdeborová

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