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 (WiNTECH, 2017)

Generalisation error in learning with random features and the hidden manifold model. - Federica Gerace; Bruno Loureiro; Florent Krzakala; Marc Mézard; Lenka Zdeborová (ICML, 2020)

To Boldly Dive Where No One Has Gone Before: Experiments in Coordinated Robotic Ocean Exploration. - José Pinto 001; 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; et al. (ISER, 2020)

Exact asymptotics for phase retrieval and compressed sensing with random generative priors. - Benjamin Aubin; Bruno Loureiro; Antoine Baker; Florent Krzakala; Lenka Zdeborová (MSML, 2020)

Phase retrieval in high dimensions: Statistical and computational phase transitions. - Antoine Maillard; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2020)

The Gaussian equivalence of generative models for learning with shallow neural networks. - Sebastian Goldt; Bruno Loureiro; Galen Reeves; Florent Krzakala; Marc Mézard; Lenka Zdeborová (MSML, 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; Cédric Gerbelot; Hugo Cui; Sebastian Goldt; Florent Krzakala; Marc Mézard; Lenka Zdeborová (NeurIPS, 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 (NeurIPS, 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 Generalised Linear Models: Precise Asymptotics in High-dimensions. - Bruno Loureiro; Gabriele Sicuro; Cédric Gerbelot; Alessandro Pacco; Florent Krzakala; Lenka Zdeborová (CoRR, 2021)

The Spiked Matrix Model With Generative Priors. - Benjamin Aubin; Bruno Loureiro; Antoine Maillard; Florent Krzakala; Lenka Zdeborová (IEEE Trans. Inf. Theory, 2021)

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

Secure Coding via Gaussian Random Fields. - Ali Bereyhi; Bruno Loureiro; Florent Krzakala; Ralf R. Müller; Hermann Schulz-Baldes (ISIT, 2022)

Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap. - Luca Pesce; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2022)

Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks. - Rodrigo Veiga 001; Ludovic Stephan; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2022)

Error Rates for Kernel Classification under Source and Capacity Conditions. - Hugo Cui; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (CoRR, 2022)

Theoretical characterization of uncertainty in high-dimensional linear classification. - 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)

A study of uncertainty quantification in overparametrized high-dimensional models. - Lucas Clarté; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (CoRR, 2022)

Coordinated Robotic Exploration of Dynamic Open Ocean Phenomena. - José Pinto 001; 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; et al. (Field Robotics, 2022)

On double-descent in uncertainty quantification in overparametrized models. - Lucas Clarté; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (AISTATS, 2023)

From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks. - Luca Arnaboldi 002; Ludovic Stephan; Florent Krzakala; Bruno Loureiro (COLT, 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 (ICML, 2023)

Universality laws for Gaussian mixtures in generalized linear models. - Yatin Dandi; Ludovic Stephan; Florent Krzakala; Bruno Loureiro; Lenka Zdeborová (NeurIPS, 2023)

Expectation consistency for calibration of neural networks. - Lucas Clarté; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (UAI, 2023)

Learning Two-Layer Neural Networks, One (Giant) Step at a Time. - Yatin Dandi; Florent Krzakala; Bruno Loureiro; Luca Pesce; Ludovic Stephan (CoRR, 2023)

Escaping mediocrity: how two-layer networks learn hard single-index models with SGD. - Luca Arnaboldi 002; Florent Krzakala; Bruno Loureiro; Ludovic Stephan (CoRR, 2023)

High-dimensional robust regression under heavy-tailed data: Asymptotics and Universality. - Urte Adomaityte; Leonardo Defilippis; Bruno Loureiro; Gabriele Sicuro (CoRR, 2023)

Learning curves for the multi-class teacher-student perceptron. - Elisabetta Cornacchia; Francesca Mignacco; Rodrigo Veiga 001; Cédric Gerbelot; Bruno Loureiro; Lenka Zdeborová (Mach. Learn. Sci. Technol., 2023)

Error scaling laws for kernel classification under source and capacity conditions. - Hugo Cui; Bruno Loureiro; Florent Krzakala; Lenka Zdeborová (Mach. Learn. Sci. Technol., 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, 2023)

Online Learning and Information Exponents: The Importance of Batch size & Time/Complexity Tradeoffs. - Luca Arnaboldi 002; Yatin Dandi; Florent Krzakala; Bruno Loureiro; Luca Pesce; Ludovic Stephan (ICML, 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 (ICML, 2024)

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

Dimension-free deterministic equivalents and scaling laws for random feature regression. - Leonardo Defilippis; Bruno Loureiro; Theodor Misiakiewicz (NeurIPS, 2024)

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

A High Dimensional Model for Adversarial Training: Geometry and Trade-Offs. - Kasimir Tanner; Matteo Vilucchio; Bruno Loureiro; Florent Krzakala (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)

Dimension-free deterministic equivalents for random feature regression. - Leonardo Defilippis; Bruno Loureiro; Theodor Misiakiewicz (CoRR, 2024)

Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs. - Luca Arnaboldi 002; Yatin Dandi; Florent Krzakala; Bruno Loureiro; Luca Pesce; Ludovic Stephan (CoRR, 2024)

A theoretical perspective on mode collapse in variational inference. - Roman Soletskyi; Marylou Gabrié; Bruno Loureiro (CoRR, 2024)

On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds. - Matteo Vilucchio; Nikolaos Tsilivis 002; Bruno Loureiro; Julia Kempe (CoRR, 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 (AISTATS, 2025)

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

Fundamental computational limits of weak learnability in high-dimensional multi-index models. - Emanuele Troiani; Yatin Dandi; Leonardo Defilippis; Lenka Zdeborová; Bruno Loureiro; Florent Krzakala (AISTATS, 2025)

Optimal Spectral Transitions in High-Dimensional Multi-Index Models. - Leonardo Defilippis; Yatin Dandi; Pierre Mergny; Florent Krzakala; Bruno Loureiro (CoRR, 2025)

Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks. - Luca Arnaboldi 002; Bruno Loureiro; Ludovic Stephan; Florent Krzakala; Lenka Zdeborová (CoRR, 2025)

On the existence of consistent adversarial attacks in high-dimensional linear classification. - Matteo Vilucchio; Lenka Zdeborová; Bruno Loureiro (CoRR, 2025)