Florent Krzakala

ORCID

Publication Activity (10 Years)

Years Active: 2004-2024
Publications (10 Years): 202

Top Venues

Publications

Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. ICLR (2024)
Matteo Vilucchio, Emanuele Troiani, Vittorio Erba, Florent Krzakala
Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers. AISTATS (2024)
Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
Asymptotics of feature learning in two-layer networks after one gradient-step. CoRR (2024)
Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Luca Pesce, Ludovic Stephan
Repetita Iuvant: Data Repetition Allows SGD to Learn High-Dimensional Multi-Index Functions. CoRR (2024)
Pierre Mergny, Justin Ko, Florent Krzakala, Lenka Zdeborová
Fundamental limits of Non-Linear Low-Rank Matrix Estimation. CoRR (2024)
Pierre Mergny, Justin Ko, Florent Krzakala, Lenka Zdeborová
Fundamental Limits of Non-Linear Low-Rank Matrix Estimation. COLT (2024)
Hugo Cui, Freya Behrens, Florent Krzakala, Lenka Zdeborová
A phase transition between positional and semantic learning in a solvable model of dot-product attention. CoRR (2024)
Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs. CoRR (2024)
Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression. CoRR (2024)
Yatin Dandi, Emanuele Troiani, Luca Arnaboldi, Luca Pesce, Lenka Zdeborová, Florent Krzakala
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents. CoRR (2024)
Cédric Gerbelot, Emanuele Troiani, Francesca Mignacco, Florent Krzakala, Lenka Zdeborová
Rigorous Dynamical Mean-Field Theory for Stochastic Gradient Descent Methods. SIAM J. Math. Data Sci. 6 (2) (2024)
Pierre Mergny, Justin Ko, Florent Krzakala
Spectral Phase Transition and Optimal PCA in Block-Structured Spiked models. CoRR (2024)
Kasimir Tanner, Matteo Vilucchio, Bruno Loureiro, Florent Krzakala
A High Dimensional Model for Adversarial Training: Geometry and Trade-Offs. CoRR (2024)
Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborová, Bruno Loureiro, Florent Krzakala
Fundamental limits of weak learnability in high-dimensional multi-index models. CoRR (2024)
Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
Analysis of learning a flow-based generative model from limited sample complexity. CoRR (2023)
D. Barbier, Carlo Lucibello, Luca Saglietti, Florent Krzakala, Lenka Zdeborová
Compressed sensing with ℓ0-norm: statistical physics analysis & algorithms for signal recovery. ITW (2023)
Antoine Baker, Florent Krzakala, Benjamin Aubin, Lenka Zdeborová
Tree-AMP: Compositional Inference with Tree Approximate Message Passing. J. Mach. Learn. Res. 24 (2023)
Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan
Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation. CoRR (2023)
Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
On double-descent in uncertainty quantification in overparametrized models. AISTATS (2023)
Aleksandr Pak, Justin Ko, Florent Krzakala
Optimal Algorithms for the Inhomogeneous Spiked Wigner Model. CoRR (2023)
Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Error scaling laws for kernel classification under source and capacity conditions. Mach. Learn. Sci. Technol. 4 (3) (2023)
D. Barbier, Carlo Lucibello, Luca Saglietti, Florent Krzakala, Lenka Zdeborová
Compressed sensing with l0-norm: statistical physics analysis and algorithms for signal recovery. CoRR (2023)
Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Expectation consistency for calibration of neural networks. UAI (2023)
Aleksandr Pak, Justin Ko, Florent Krzakala
Optimal Algorithms for the Inhomogeneous Spiked Wigner Model. NeurIPS (2023)
Cédric Gerbelot, Alia Abbara, Florent Krzakala
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (Or: How to Prove Kabashima's Replica Formula). IEEE Trans. Inf. Theory 69 (3) (2023)
Ali Bereyhi, Bruno Loureiro, Florent Krzakala, Ralf R. Müller, Hermann Schulz-Baldes
Bayesian Inference With Nonlinear Generative Models: Comments on Secure Learning. IEEE Trans. Inf. Theory 69 (12) (2023)
Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan
Learning Two-Layer Neural Networks, One (Giant) Step at a Time. CoRR (2023)
Sebastian Goldt, Florent Krzakala, Lenka Zdeborová, Nicolas Brunel
Bayesian reconstruction of memories stored in neural networks from their connectivity. PLoS Comput. Biol. 19 (1) (2023)
Luca Arnaboldi, Florent Krzakala, Bruno Loureiro, Ludovic Stephan
Escaping mediocrity: how two-layer networks learn hard single-index models with SGD. CoRR (2023)
Vittorio Erba, Florent Krzakala, Rodrigo Pérez, Lenka Zdeborová
Statistical mechanics of the maximum-average submatrix problem. CoRR (2023)
Davide Ghio, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective. CoRR (2023)
Hugo Cui, Florent Krzakala, Lenka Zdeborová
Optimal Learning of Deep Random Networks of Extensive-width. CoRR (2023)
Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation. ICML (2023)
Yatin Dandi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro, Lenka Zdeborová
Universality laws for Gaussian mixtures in generalized linear models. NeurIPS (2023)
Hugo Cui, Florent Krzakala, Lenka Zdeborová
Bayes-optimal Learning of Deep Random Networks of Extensive-width. ICML (2023)
Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks. COLT (2023)
Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Expectation consistency for calibration of neural networks. CoRR (2023)
Matteo Vilucchio, Emanuele Troiani, Vittorio Erba, Florent Krzakala
Asymptotic Characterisation of Robust Empirical Risk Minimisation Performance in the Presence of Outliers. CoRR (2023)
Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks. CoRR (2023)
Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. CoRR (2022)
Federica Gerace, Florent Krzakala, Bruno Loureiro, Ludovic Stephan, Lenka Zdeborová
Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension. CoRR (2022)
Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Error Rates for Kernel Classification under Source and Capacity Conditions. CoRR (2022)
Cédric Gerbelot, Emanuele Troiani, Francesca Mignacco, Florent Krzakala, Lenka Zdeborová
Rigorous dynamical mean field theory for stochastic gradient descent methods. CoRR (2022)
Ali Bereyhi, Bruno Loureiro, Florent Krzakala, Ralf R. Müller, Hermann Schulz-Baldes
Secure Coding via Gaussian Random Fields. CoRR (2022)
Luca Pesce, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap. NeurIPS (2022)
Emanuele Troiani, Vittorio Erba, Florent Krzakala, Antoine Maillard, Lenka Zdeborová
Optimal denoising of rotationally invariant rectangular matrices. CoRR (2022)
Max Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová
Multi-layer State Evolution Under Random Convolutional Design. CoRR (2022)
Emanuele Troiani, Vittorio Erba, Florent Krzakala, Antoine Maillard, Lenka Zdeborová
Optimal denoising of rotationally invariant rectangular matrices. MSML (2022)
Luca Pesce, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Subspace clustering in high-dimensions: Phase transitions \& Statistical-to-Computational gap. CoRR (2022)
Max Daniels, Cédric Gerbelot, Florent Krzakala, Lenka Zdeborová
Multi-layer State Evolution Under Random Convolutional Design. NeurIPS (2022)
Alessandro Cappelli, Ruben Ohana, Julien Launay, Laurent Meunier, Iacopo Poli, Florent Krzakala
Adversarial Robustness by Design Through Analog Computing And Synthetic Gradients. ICASSP (2022)
Ali Bereyhi, Bruno Loureiro, Florent Krzakala, Ralf R. Müller, Hermann Schulz-Baldes
Secure Coding via Gaussian Random Fields. ISIT (2022)
Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
A study of uncertainty quantification in overparametrized high-dimensional models. CoRR (2022)
Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Theoretical characterization of uncertainty in high-dimensional linear classification. CoRR (2022)
Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension. ICML (2022)
Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks. CoRR (2022)
Ali Bereyhi, Bruno Loureiro, Florent Krzakala, Ralf R. Müller, Hermann Schulz-Baldes
Bayesian Inference with Nonlinear Generative Models: Comments on Secure Learning. CoRR (2022)
Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks. NeurIPS (2022)
Sebastian Goldt, Bruno Loureiro, Galen Reeves, Florent Krzakala, Marc Mézard, Lenka Zdeborová
The Gaussian equivalence of generative models for learning with shallow neural networks. MSML (2021)
Antoine Maillard, Florent Krzakala, Marc Mézard, Lenka Zdeborová
Perturbative construction of mean-field equations in extensive-rank matrix factorization and denoising. CoRR (2021)
Maria Refinetti, Sebastian Goldt, Florent Krzakala, Lenka Zdeborová
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed. CoRR (2021)
Charles Brossollet, Alessandro Cappelli, Igor Carron, Charidimos Chaintoutis, Amélie Chatelain, Laurent Daudet, Sylvain Gigan, Daniel Hesslow, Florent Krzakala, Julien Launay, Safa Mokaadi, Fabien Moreau, Kilian Müller, Ruben Ohana, Gustave Pariente, Iacopo Poli, Giuseppe Luca Tommasone
LightOn Optical Processing Unit: Scaling-up AI and HPC with a Non von Neumann co-processor. CoRR (2021)
Antoine Maillard, Florent Krzakala, Yue M. Lu, Lenka Zdeborová
Construction of optimal spectral methods in phase retrieval. MSML (2021)
Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová
Learning curves of generic features maps for realistic datasets with a teacher-student model. NeurIPS (2021)
Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime. NeurIPS (2021)
Maria Refinetti, Sebastian Goldt, Florent Krzakala, Lenka Zdeborová
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed. ICML (2021)
Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová
The Spiked Matrix Model With Generative Priors. IEEE Trans. Inf. Theory 67 (2) (2021)
Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions. NeurIPS (2021)
Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime. CoRR (2021)
Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová
Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions. CoRR (2021)
Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová
Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model. CoRR (2021)
Charles Brossollet, Alessandro Cappelli, Igor Carron, Charidimos Chaintoutis, Amélie Chatelain, Laurent Daudet, Sylvain Gigan, Daniel Hesslow, Florent Krzakala, Julien Launay, Safa Mokaadi, Fabien Moreau, Kilian Müller, Ruben Ohana, Gustave Pariente, Iacopo Poli, Giuseppe Luca Tommasone
LightOn Optical Processing Unit : Scaling-up AI and HPC with a Non von Neumann co-processor. HCS (2021)
Alessandro Cappelli, Ruben Ohana, Julien Launay, Laurent Meunier, Iacopo Poli, Florent Krzakala
Adversarial Robustness by Design through Analog Computing and Synthetic Gradients. CoRR (2021)
Jean Barbier, Nicolas Macris, Mohamad Dia, Florent Krzakala
Mutual Information and Optimality of Approximate Message-Passing in Random Linear Estimation. IEEE Trans. Inf. Theory 66 (7) (2020)
Benjamin Aubin, Florent Krzakala, Yue M. Lu, Lenka Zdeborová
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization. NeurIPS (2020)
Francesca Mignacco, Florent Krzakala, Yue Lu, Pierfrancesco Urbani, Lenka Zdeborová
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture. ICML (2020)
Mushegh Rafayelyan, Jonathan Dong, Yongqi Tan, Florent Krzakala, Sylvain Gigan
Large-Scale Optical Reservoir Computing for Spatiotemporal Chaotic Systems Prediction. CoRR (2020)
Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures. NeurIPS (2020)
Antoine Maillard, Florent Krzakala, Yue M. Lu, Lenka Zdeborová
Construction of optimal spectral methods in phase retrieval. CoRR (2020)
Antoine Baker, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová
TRAMP: Compositional Inference with TRee Approximate Message Passing. CoRR (2020)
Antoine Baker, Indaco Biazzo, Alfredo Braunstein, Giovanni Catania, Luca Dall'Asta, Alessandro Ingrosso, Florent Krzakala, Fabio Mazza, Marc Mézard, Anna Paola Muntoni, Maria Refinetti, Stefano Sarao Mannelli, Lenka Zdeborová
Epidemic mitigation by statistical inference from contact tracing data. CoRR (2020)
Cédric Gerbelot, Alia Abbara, Florent Krzakala
Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices. COLT (2020)
Julien Launay, Iacopo Poli, Kilian Müller, Gustave Pariente, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
Hardware Beyond Backpropagation: a Photonic Co-Processor for Direct Feedback Alignment. CoRR (2020)
Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová
Exact asymptotics for phase retrieval and compressed sensing with random generative priors. MSML (2020)
Jonathan Dong, Ruben Ohana, Mushegh Rafayelyan, Florent Krzakala
Reservoir Computing meets Recurrent Kernels and Structured Transforms. NeurIPS (2020)
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. NeurIPS (2020)
Sebastian Goldt, Galen Reeves, Marc Mézard, Florent Krzakala, Lenka Zdeborová
The Gaussian equivalence of generative models for learning with two-layer neural networks. CoRR (2020)
Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová
Generalisation error in learning with random features and the hidden manifold model. ICML (2020)
Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification. NeurIPS (2020)
Julien Launay, Iacopo Poli, Kilian Müller, Igor Carron, Laurent Daudet, Florent Krzakala, Sylvain Gigan
Light-in-the-loop: using a photonics co-processor for scalable training of neural networks. CoRR (2020)
Stéphane d'Ascoli, Maria Refinetti, Giulio Biroli, Florent Krzakala
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime. CoRR (2020)
Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Phase retrieval in high dimensions: Statistical and computational phase transitions. NeurIPS (2020)
Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
Phase retrieval in high dimensions: Statistical and computational phase transitions. CoRR (2020)
Francesca Mignacco, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification. CoRR (2020)
Stefano Sarao Mannelli, Giulio Biroli, Chiara Cammarota, Florent Krzakala, Pierfrancesco Urbani, Lenka Zdeborová
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval. CoRR (2020)
Francesca Mignacco, Florent Krzakala, Yue M. Lu, Lenka Zdeborová
The role of regularization in classification of high-dimensional noisy Gaussian mixture. CoRR (2020)
Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures. CoRR (2020)
Cédric Gerbelot, Alia Abbara, Florent Krzakala
Asymptotic Errors for Teacher-Student Convex Generalized Linear Models (or : How to Prove Kabashima's Replica Formula). CoRR (2020)
Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová
Generalisation error in learning with random features and the hidden manifold model. CoRR (2020)
Alia Abbara, Benjamin Aubin, Florent Krzakala, Lenka Zdeborová
Rademacher complexity and spin glasses: A link between the replica and statistical theories of learning. MSML (2020)

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Publication Activity (10 Years)

Top Topics

Top Venues

Florent Krzakala