Loureiro, Bruno

31 publications

AISTATS 2025 A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs Kasimir Tanner, Matteo Vilucchio, Bruno Loureiro, Florent Krzakala

AISTATS 2025 A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue Lu, Bruno Loureiro

NeurIPS 2025 Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks Luca Arnaboldi, Bruno Loureiro, Ludovic Stephan, Florent Krzakala, Lenka Zdeborova

AISTATS 2025 Fundamental Computational Limits of Weak Learnability in High-Dimensional Multi-Index Models Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborova, Bruno Loureiro, Florent Krzakala

NeurIPS 2025 Optimal Spectral Transitions in High-Dimensional Multi-Index Models Leonardo Defilippis, Yatin Dandi, Pierre Mergny, Florent Krzakala, Bruno Loureiro

UAI 2024 Analysis of Bootstrap and Subsampling in High-Dimensional Regularized Regression Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

ICML 2024 Asymptotics of Feature Learning in Two-Layer Networks After One Gradient-Step Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue Lu, Lenka Zdeborova, Bruno Loureiro

ICML 2024 Asymptotics of Learning with Deep Structured (Random) Features Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro

NeurIPS 2024 Dimension-Free Deterministic Equivalents and Scaling Laws for Random Feature Regression Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz

ICMLW 2024 Fundamental Limits of Weak Learnability in High-Dimensional Multi-Index Models Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborova, Bruno Loureiro, Florent Krzakala

JMLR 2024 How Two-Layer Neural Networks Learn, One (Giant) Step at a Time Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

ICML 2024 Online Learning and Information Exponents: The Importance of Batch Size & Time/Complexity Tradeoffs Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

ICML 2023 Are Gaussian Data All You Need? the Extents and Limits of Universality in High-Dimensional Generalized Linear Estimation Luca Pesce, Florent Krzakala, Bruno Loureiro, Ludovic Stephan

ICML 2023 Deterministic Equivalent and Error Universality of Deep Random Features Learning Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro

NeurIPSW 2023 Escaping Mediocrity: How Two-Layer Networks Learn Hard Generalized Linear Models Luca Arnaboldi, Florent Krzakala, Bruno Loureiro, Ludovic Stephan

UAI 2023 Expectation Consistency for Calibration of Neural Networks Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

COLT 2023 From High-Dimensional & Mean-Field Dynamics to Dimensionless ODEs: A Unifying Approach to SGD in Two-Layers Networks Luca Arnaboldi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro

NeurIPSW 2023 High-Dimensional Robust Regression Under Heavy-Tailed Data: Asymptotics and Universality Urte Adomaityte, Leonardo Defilippis, Bruno Loureiro, Gabriele Sicuro

NeurIPSW 2023 How Two-Layer Neural Networks Learn, One (Giant) Step at a Time Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

AISTATS 2023 On Double-Descent in Uncertainty Quantification in Overparametrized Models Lucas Clarte, Bruno Loureiro, Florent Krzakala, Lenka Zdeborova

NeurIPS 2023 Universality Laws for Gaussian Mixtures in Generalized Linear Models Yatin Dandi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro, Lenka Zdeborová

ICML 2022 Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension Bruno Loureiro, Cedric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala

NeurIPS 2022 Phase Diagram of Stochastic Gradient Descent in High-Dimensional Two-Layer Neural Networks Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

NeurIPS 2022 Subspace Clustering in High-Dimensions: Phase Transitions & Statistical-to-Computational Gap Luca Pesce, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

NeurIPS 2021 Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

NeurIPS 2021 Learning Curves of Generic Features Maps for Realistic Datasets with a Teacher-Student Model Bruno Loureiro, Cedric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mezard, Lenka Zdeborová

NeurIPS 2021 Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-Dimensions Bruno Loureiro, Gabriele Sicuro, Cedric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová

ICML 2020 Generalisation Error in Learning with Random Features and the Hidden Manifold Model Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mezard, Lenka Zdeborova

NeurIPS 2020 Phase Retrieval in High Dimensions: Statistical and Computational Phase Transitions Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

NeurIPSW 2019 Precise Asymptotics for Phase Retrieval and Compressed Sensing with Random Generative Priors Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborova

NeurIPS 2019 The Spiked Matrix Model with Generative Priors Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová