Florent Krzakala

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 Thouless-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