Lenka Zdeborová

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Related publications (72)

Probing transfer learning with a model of synthetic correlated datasets

Transfer learning can signiﬁcantly improve the sample eﬃciency of neural networks, by exploiting the relatedness between a data-scarce target task and a data-abundant source task. Despite years of successful applications, transfer learning practice often r ...

2022

Large deviations of semisupervised learning in the stochastic block model

Lenka Zdeborová, Hugo Chao Cui, Luca Saglietti

In semisupervised community detection, the membership of a set of revealed nodes is known in addition to the graph structure and can be leveraged to achieve better inference accuracies. While previous works investigated the case where the revealed nodes ar ...

AMER PHYSICAL SOC2022

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

Florent Gérard Krzakala, Lenka Zdeborová

Factorization of matrices where the rank of the two factors diverges linearly with their sizes has many applications in diverse areas such as unsupervised representation learning, dictionary learning or sparse coding. We consider a setting where the two fa ...

2022

Disordered systems insights on computational hardness

Lenka Zdeborová

In this review article we discuss connections between the physics of disordered systems, phase transitions in inference problems, and computational hardness. We introduce two models representing the behavior of glassy systems, the spiked tensor model and t ...

2022

Planted XY model: Thermodynamics and inference

Lenka Zdeborová, Guanhao Huang, Siyu Chen, Giovanni Piccioli

In this paper we study a fully connected planted spin glass named the planted XY model. Motivation for studying this system comes both from the spin glass field and the one of statistical inference where it models the angular synchronization problem. We de ...

2022

Multi-layer State Evolution Under Random Convolutional Design

Florent Gérard Krzakala, Lenka Zdeborová

Signal recovery under generative neural network priors has emerged as a promising direction in statistical inference and computational imaging. Theoretical analysis of reconstruction algorithms under generative priors is, however, challenging. For generati ...

2022

(Dis)assortative partitions on random regular graphs

Lenka Zdeborová, Freya Behrens, Yaroslav Kivva

We study the problem of assortative and disassortative partitions on random dregular graphs. Nodes in the graph are partitioned into two non-empty groups. In the assortative partition every node requires at least H of their neighbors to be in their own gro ...

IOP Publishing Ltd2022

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

Florent Gérard Krzakala, Lenka Zdeborová, Luca Pesce, Bruno Loureiro, Bruno Loureiro

A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...

2022

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

Florent Gérard Krzakala, Lenka Zdeborová, Bruno Loureiro, Bruno Loureiro, Ludovic Théo Stephan

Despite the non-convex optimization landscape, over-parametrized shallow networks are able to achieve global convergence under gradient descent. The picture can be radically different for narrow net-works, which tend to get stuck in badly-generalizing loca ...

2022

Epidemic mitigation by statistical inference from contact tracing data

Florent Gérard Krzakala, Lenka Zdeborová, Maria Refinetti, Indaco Biazzo, Giovanni Catania

Contact tracing is an essential tool to mitigate the impact of a pandemic, such as the COVID-19 pandemic. In order to achieve efficient and scalable contact tracing in real time, digital devices can play an important role. While a lot of attention has been ...

National Academy of Sciences2021

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