Bruno Loureiro

According to our database, Bruno Loureiro authored at least 44 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities.

Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, Bruno Loureiro

CoRR, 2024

On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds.

Matteo Vilucchio, Nikolaos Tsilivis, Bruno Loureiro, Julia Kempe

CoRR, 2024

A theoretical perspective on mode collapse in variational inference.

Roman Soletskyi, Marylou Gabrié, Bruno Loureiro

CoRR, 2024

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs.

Luca Arnaboldi, Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

CoRR, 2024

Dimension-free deterministic equivalents for random feature regression.

Leonardo Defilippis, Bruno Loureiro, Theodor Misiakiewicz

CoRR, 2024

Fundamental limits of weak learnability in high-dimensional multi-index models.

Emanuele Troiani, Yatin Dandi, Leonardo Defilippis, Lenka Zdeborová, Bruno Loureiro, Florent Krzakala

CoRR, 2024

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression.

Lucas Clarté, Adrien Vandenbroucque, Guillaume Dalle, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

CoRR, 2024

A High Dimensional Model for Adversarial Training: Geometry and Trade-Offs.

Kasimir Tanner, Matteo Vilucchio, Bruno Loureiro, Florent Krzakala

CoRR, 2024

Asymptotics of Learning with Deep Structured (Random) Features.

Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Asymptotics of feature learning in two-layer networks after one gradient-step.

Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro

Proceedings of the Forty-first International Conference on Machine Learning, 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

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Bayesian Inference With Nonlinear Generative Models: Comments on Secure Learning.

Ali Bereyhi, Bruno Loureiro, Florent Krzakala, Ralf R. Müller, Hermann Schulz-Baldes

IEEE Trans. Inf. Theory, December, 2023

Error scaling laws for kernel classification under source and capacity conditions.

Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Mach. Learn. Sci. Technol., September, 2023

Learning curves for the multi-class teacher-student perceptron.

Elisabetta Cornacchia, Francesca Mignacco, Rodrigo Veiga, Cédric Gerbelot, Bruno Loureiro, Lenka Zdeborová

Mach. Learn. Sci. Technol., March, 2023

High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality.

Urte Adomaityte, Leonardo Defilippis, Bruno Loureiro, Gabriele Sicuro

CoRR, 2023

Escaping mediocrity: how two-layer networks learn hard single-index models with SGD.

Luca Arnaboldi, Florent Krzakala, Bruno Loureiro, Ludovic Stephan

CoRR, 2023

Learning Two-Layer Neural Networks, One (Giant) Step at a Time.

Yatin Dandi, Florent Krzakala, Bruno Loureiro, Luca Pesce, Ludovic Stephan

CoRR, 2023

Expectation consistency for calibration of neural networks.

Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Universality laws for Gaussian mixtures in generalized linear models.

Yatin Dandi, Ludovic Stephan, Florent Krzakala, Bruno Loureiro, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Deterministic equivalent and error universality of deep random features learning.

Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

On double-descent in uncertainty quantification in overparametrized models.

Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Coordinated Robotic Exploration of Dynamic Open Ocean Phenomena.

José Pinto, Maria Costa, Renato Mendes, Keila Lima, Paulo Sousa Dias, João Pereira, Manuel A. Ribeiro, Renato Campos, Maria Paola Tomasino, Catarina Magalhães, Francisco López Castejón, Javier Gilabert, Adriana M. Santos-Ferreira, José C. B. da Silva, Paulo Relvas, Trent Lukaczyk, Kay Arne Skarpnes, Emlyn J. Davies, Alexander Chekalyuk, Bruno Loureiro, Ian G. Brosnan, Jing Li, João Borges de Sousa, Kanna Rajan

Field Robotics, March, 2022

A study of uncertainty quantification in overparametrized high-dimensional models.

Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

CoRR, 2022

Gaussian Universality of Linear Classifiers with Random Labels in High-Dimension.

Federica Gerace, Florent Krzakala, Bruno Loureiro, Ludovic Stephan, Lenka Zdeborová

CoRR, 2022

Theoretical characterization of uncertainty in high-dimensional linear classification.

Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

CoRR, 2022

Error Rates for Kernel Classification under Source and Capacity Conditions.

Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

CoRR, 2022

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

Rodrigo Veiga, Ludovic Stephan, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Luca Pesce, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Secure Coding via Gaussian Random Fields.

Ali Bereyhi, Bruno Loureiro, Florent Krzakala, Ralf R. Müller, Hermann Schulz-Baldes

Proceedings of the IEEE International Symposium on Information Theory, 2022

Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension.

Bruno Loureiro, Cédric Gerbelot, Maria Refinetti, Gabriele Sicuro, Florent Krzakala

Proceedings of the International Conference on Machine Learning, 2022

2021

The Spiked Matrix Model With Generative Priors.

Benjamin Aubin, Bruno Loureiro, Antoine Maillard, Florent Krzakala, Lenka Zdeborová

IEEE Trans. Inf. Theory, 2021

Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions.

Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová

CoRR, 2021

Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model.

Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová

CoRR, 2021

Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions.

Bruno Loureiro, Gabriele Sicuro, Cédric Gerbelot, Alessandro Pacco, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning curves of generic features maps for realistic datasets with a teacher-student model.

Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime.

Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

The Gaussian equivalence of generative models for learning with shallow neural networks.

Sebastian Goldt, Bruno Loureiro, Galen Reeves, Florent Krzakala, Marc Mézard, Lenka Zdeborová

Proceedings of the Mathematical and Scientific Machine Learning, 2021

2020

Phase retrieval in high dimensions: Statistical and computational phase transitions.

Antoine Maillard, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Exact asymptotics for phase retrieval and compressed sensing with random generative priors.

Benjamin Aubin, Bruno Loureiro, Antoine Baker, Florent Krzakala, Lenka Zdeborová

Proceedings of Mathematical and Scientific Machine Learning, 2020

To Boldly Dive Where No One Has Gone Before: Experiments in Coordinated Robotic Ocean Exploration.

José Pinto, Maria Costa, Keila Lima, Paulo Sousa Dias, João Pereira, Manuel A. Ribeiro, Renato Campos, Zara Mirmalek, Renato Mendes, Francisco López Castejón, Javier Gilabert, Maria Paola Tomasino, Catarina Magalhães, José C. B. da Silva, Paulo Relvas, Trent Lukaczyk, Kay Arne Skarpnes, Martin Ludvigsen, Alexander Chekalyuk, Bruno Loureiro, Ian G. Brosnan, Jing Li, Ami Hannon, João Borges de Sousa, Kanna Rajan

Proceedings of the Experimental Robotics - The 17th International Symposium, 2020

Generalisation error in learning with random features and the hidden manifold model.

Federica Gerace, Bruno Loureiro, Florent Krzakala, Marc Mézard, Lenka Zdeborová

Proceedings of the 37th International Conference on Machine Learning, 2020

2017

EVERUN: Enabling Power Consumption Monitoring in Underwater Networking Platforms.

Giannis Kazdaridis, Stratos Keranidis, Polychronis Symeonidis, Paulo Sousa Dias, Pedro Gonçalves, Bruno Loureiro, Petrika Gjanci, Chiara Petrioli

Proceedings of the 11th Workshop on Wireless Network Testbeds, 2017