← Authors

Florent Krzakala

55 papers · 2013–2025 · 7 conferences · across top CS/AI conferences

Achievements

🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🌍 Conference Polyglot (7) πŸƒ Academic Marathon (12) 🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (6) 🏠 Conference Loyalist (26) 🧬 Topic Evolution πŸ† Keyword Champion (11) πŸ‘‘ Triple Crown 🀝 Dynamic Duo (40) πŸ”¬ Deep Specialist (19) πŸ”₯ Unstoppable (8) ⚑ Prolific Year (8) πŸ“ˆ Trend Setter πŸ’Ž Century Club (55) πŸ—ƒοΈ Keyword Collector (179) ❓ The Questioner (2)

Conferences

NIPS (26) ICML (14) AISTATS (5) COLT (5) JMLR (2) UAI (2) ICLR (1)

Top co-authors

Lenka ZdeborovΓ‘ (40) Bruno Loureiro (21) Yatin Dandi (8) Hugo Cui (8) Luca Pesce (7) Ludovic Stephan (6) Emanuele Troiani (5) Cedric Gerbelot (5) Antoine Maillard (5) jean barbier (4)

Research topics

Probability (1)

Keywords

generalization error (11) approximate message passing (11) neural network (8) phase transition (5) high-dimensional analysis (5) high-dimensional statistics (5) double descent (4) statistical learning (4) compressed sensing (4) gaussian mixture (4) message passing (4) state evolution (4) stochastic gradient descent (4) generalized linear model (4) gradient descent (3) spectral method (3) gradient flow (3) convex loss (2) non-convex optimization (2) kernel regression (2)

Papers

A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs AISTATS 2025
Fundamental computational limits of weak learnability in high-dimensional multi-index models AISTATS 2025
A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities AISTATS 2025
Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications COLT 2025
Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds ICML 2025
Asymptotics of feature learning in two-layer networks after one gradient-step ICML 2024
Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs ICML 2024
Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity ICLR 2024
Fundamental Limits of Non-Linear Low-Rank Matrix Estimation COLT 2024
Asymptotic Characterisation of the Performance of Robust Linear Regression in the Presence of Outliers AISTATS 2024
A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention NIPS 2024
Bayes-optimal learning of an extensive-width neural network from quadratically many samples NIPS 2024
Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression UAI 2024
How Two-Layer Neural Networks Learn, One (Giant) Step at a Time JMLR 2024
Spectral Phase Transition and Optimal PCA in Block-Structured Spiked Models ICML 2024
The Benefits of Reusing Batches for Gradient Descent in Two-Layer Networks: Breaking the Curse of Information and Leap Exponents ICML 2024
From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks COLT 2023
Universality laws for Gaussian mixtures in generalized linear models NIPS 2023
Optimal Algorithms for the Inhomogeneous Spiked Wigner Model NIPS 2023
On double-descent in uncertainty quantification in overparametrized models AISTATS 2023
Bayes-optimal Learning of Deep Random Networks of Extensive-width ICML 2023
Are Gaussian Data All You Need? The Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation ICML 2023
Tree-AMP: Compositional Inference with Tree Approximate Message Passing JMLR 2023
Expectation consistency for calibration of neural networks UAI 2023
Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks NIPS 2022
Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension ICML 2022
Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap NIPS 2022
Multi-layer State Evolution Under Random Convolutional Design NIPS 2022
Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed ICML 2021
Learning curves of generic features maps for realistic datasets with a teacher-student model NIPS 2021
Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions NIPS 2021
Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime NIPS 2021
Generalisation error in learning with random features and the hidden manifold model ICML 2020
The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture ICML 2020
Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime ICML 2020
Reservoir Computing meets Recurrent Kernels and Structured Transforms NIPS 2020
Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization NIPS 2020
Phase retrieval in high dimensions: Statistical and computational phase transitions NIPS 2020
Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification NIPS 2020
Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures NIPS 2020
Asymptotic Errors for High-Dimensional Convex Penalized Linear Regression beyond Gaussian Matrices COLT 2020
Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval NIPS 2020
Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models ICML 2019
The spiked matrix model with generative priors NIPS 2019
Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup NIPS 2019
Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models NIPS 2019
Optimal Errors and Phase Transitions in High-Dimensional Generalized Linear Models COLT 2018
The committee machine: Computational to statistical gaps in learning a two-layers neural network NIPS 2018
Entropy and mutual information in models of deep neural networks NIPS 2018
Mutual information for symmetric rank-one matrix estimation: A proof of the replica formula NIPS 2016
Matrix Completion from Fewer Entries: Spectral Detectability and Rank Estimation NIPS 2015
Swept Approximate Message Passing for Sparse Estimation ICML 2015
Training Restricted Boltzmann Machine via the οΏΌThouless-Anderson-Palmer free energy NIPS 2015
Spectral Clustering of graphs with the Bethe Hessian NIPS 2014
Blind Calibration in Compressed Sensing using Message Passing Algorithms NIPS 2013