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)

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)

Large deviations in the perceptron model and consequences for active learning. - Hugo Cui; Luca Saglietti; Lenka Zdeborová (Mach. Learn. Sci. Technol., 2021)

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

Bayes-optimal Learning of Deep Random Networks of Extensive-width. - Hugo Cui; Florent Krzakala; Lenka Zdeborová (ICML, 2023)

Deterministic equivalent and error universality of deep random features learning. - Dominik Schröder; Hugo Cui; Daniil Dmitriev; Bruno Loureiro (ICML, 2023)

High-dimensional Asymptotics of Denoising Autoencoders. - Hugo Cui; Lenka Zdeborová (NeurIPS, 2023)

Optimal Learning of Deep Random Networks of Extensive-width. - Hugo Cui; Florent Krzakala; Lenka Zdeborová (CoRR, 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)

Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. - Hugo Cui; Florent Krzakala; Eric Vanden-Eijnden; Lenka Zdeborová (ICLR, 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)

A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention. - Hugo Cui; Freya Behrens; Florent Krzakala; Lenka Zdeborová (NeurIPS, 2024)

High-dimensional learning of narrow neural networks. - Hugo Cui (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 precise asymptotic analysis of learning diffusion models: theory and insights. - Hugo Cui; Cengiz Pehlevan; Yue M. Lu (CoRR, 2025)

Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds. - Emanuele Troiani; Hugo Cui; Yatin Dandi; Florent Krzakala; Lenka Zdeborová (CoRR, 2025)