
The Gaussian equivalence of generative models for learning with twolayer neural networks
Understanding the impact of data structure on learning in neural network...
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Dynamical meanfield theory for stochastic gradient descent in Gaussian mixture classification
We analyze in a closed form the learning dynamics of stochastic gradient...
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The role of regularization in classification of highdimensional noisy Gaussian mixture
We consider a highdimensional mixture of two Gaussians in the noisy reg...
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Passed & Spurious: analysing descent algorithms and local minima in spiked matrixtensor model
In this work we analyse quantitatively the interplay between the loss la...
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Generalisation error in learning with random features and the hidden manifold model
We study generalised linear regression and classification for a syntheti...
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Thresholds of descending algorithms in inference problems
We review recent works on analyzing the dynamics of gradientbased algor...
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Marvels and Pitfalls of the Langevin Algorithm in Noisy Highdimensional Inference
Gradientdescentbased algorithms and their stochastic versions have wid...
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TRAMP: Compositional Inference with TRee Approximate Message Passing
We introduce tramp, standing for TRee Approximate Message Passing, a pyt...
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Approximate Survey Propagation for Statistical Inference
Approximate message passing algorithm enjoyed considerable attention in ...
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Generalisation dynamics of online learning in overparameterised neural networks
Deep neural networks achieve stellar generalisation on a variety of prob...
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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...
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Dynamics of stochastic gradient descent for twolayer neural networks in the teacherstudent setup
Deep neural networks achieve stellar generalisation even when they have ...
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Approximate messagepassing for convex optimization with nonseparable penalties
We introduce an iterative optimization scheme for convex objectives cons...
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Modelling the influence of data structure on learning in neural networks
The lack of crisp mathematical models that capture the structure of real...
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Generalization error in highdimensional perceptrons: Approaching Bayes error with convex optimization
We consider a commonly studied supervised classification of a synthetic ...
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Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions
We study the dynamics of optimization and the generalization properties ...
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The committee machine: Computational to statistical gaps in learning a twolayers neural network
Heuristic tools from statistical physics have been used in the past to l...
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Exact asymptotics for phase retrieval and compressed sensing with random generative priors
We consider the problem of compressed sensing and of (realvalued) phase...
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Entropy and mutual information in models of deep neural networks
We examine a class of deep learning models with a tractable method to co...
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Who is Afraid of Big Bad Minima? Analysis of GradientFlow in a Spiked MatrixTensor Model
Gradientbased algorithms are effective for many machine learning tasks,...
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On the Universality of Noiseless Linear Estimation with Respect to the Measurement Matrix
In a noiseless linear estimation problem, one aims to reconstruct a vect...
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Phase Transitions, Optimal Errors and Optimality of MessagePassing in Generalized Linear Models
We consider generalized linear models (GLMs) where an unknown ndimensio...
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Streaming Bayesian inference: theoretical limits and minibatch approximate messagepassing
In statistical learning for realworld largescale data problems, one mu...
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MultiLayer Generalized Linear Estimation
We consider the problem of reconstructing a signal from multilayered (p...
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Phase transitions and optimal algorithms in highdimensional Gaussian mixture clustering
We consider the problem of Gaussian mixture clustering in the highdimen...
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Fast Randomized SemiSupervised Clustering
We consider the problem of clustering partially labeled data from a mini...
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Statistical physics of inference: Thresholds and algorithms
Many questions of fundamental interest in todays science can be formulat...
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Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation
The completion of low rank matrices from few entries is a task with many...
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Phase Transitions in Sparse PCA
We study optimal estimation for sparse principal component analysis when...
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Phase transitions in semisupervised clustering of sparse networks
Predicting labels of nodes in a network, such as community memberships o...
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Message passing for quantified Boolean formulas
We introduce two types of message passing algorithms for quantified Bool...
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Phase transitions and sample complexity in Bayesoptimal matrix factorization
We analyse the matrix factorization problem. Given a noisy measurement o...
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Spectral redemption: clustering sparse networks
Spectral algorithms are classic approaches to clustering and community d...
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Model Selection for Degreecorrected Block Models
The proliferation of models for networks raises challenging problems of ...
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Comparative Study for Inference of Hidden Classes in Stochastic Block Models
Inference of hidden classes in stochastic block model is a classical pro...
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Dense Limit of the DawidSkene Model for Crowdsourcing and Regions of Suboptimality of Message Passing Algorithms
Crowdsourcing is a strategy to categorize data through the contribution ...
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On the glassy nature of the hard phase in inference problems
An algorithmically hard phase was described in a range of inference prob...
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Typology of phase transitions in Bayesian inference problems
Many inference problems, notably the stochastic block model (SBM) that g...
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Rankone matrix estimation: analysis of algorithmic and information theoretic limits by the spatial coupling method
Factorizing lowrank matrices is a problem with many applications in mac...
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The spiked matrix model with generative priors
Using a lowdimensional parametrization of signals is a generic and powe...
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Hightemperature Expansions and Message Passing Algorithms
Improved meanfield technics are a central theme of statistical physics ...
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Large deviations for the perceptron model and consequences for active learning
Active learning is a branch of machine learning that deals with problems...
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Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval
Despite the widespread use of gradientbased algorithms for optimizing h...
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Recovery thresholds in the sparse planted matching problem
We consider the statistical inference problem of recovering an unknown p...
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Phase retrieval in high dimensions: Statistical and computational phase transitions
We consider the phase retrieval problem of reconstructing a ndimensiona...
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Lenka Zdeborová
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Researcher at CNRS (Centre national de la recherche scientifique)