Hugo Cui

According to our database, Hugo Cui authored at least 21 papers between 2020 and 2026.

Collaborative distances:

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

2026

Asymptotic Theory of Iterated Empirical Risk Minimization, with Applications to Active Learning.

Hugo Cui, Yue M. Lu

CoRR, January, 2026

2025

High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification.

Nicholas Barnfield, Hugo Cui, Yue M. Lu

CoRR, September, 2025

A precise asymptotic analysis of learning diffusion models: theory and insights.

Hugo Cui, Cengiz Pehlevan, Yue M. Lu

CoRR, January, 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

Proceedings of the Forty-second International Conference on Machine Learning, 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 M. Lu, Bruno Loureiro

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2025

2024

Topics in statistical physics of high-dimensional machine learning.

Hugo Cui

PhD thesis, 2024

High-dimensional learning of narrow neural networks.

Hugo Cui

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 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

Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity.

Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová

Proceedings of the Twelfth International Conference on Learning Representations, 2024

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

Optimal Learning of Deep Random Networks of Extensive-width.

Hugo Cui, Florent Krzakala, Lenka Zdeborová

CoRR, 2023

High-dimensional Asymptotics of Denoising Autoencoders.

Hugo Cui, 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

Bayes-optimal Learning of Deep Random Networks of Extensive-width.

Hugo Cui, Florent Krzakala, Lenka Zdeborová

Proceedings of the International Conference on Machine Learning, 2023

2022

Error Rates for Kernel Classification under Source and Capacity Conditions.

Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

CoRR, 2022

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 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

2020

Large deviations for the perceptron model and consequences for active learning.

Hugo Cui, Luca Saglietti, Lenka Zdeborová

Proceedings of Mathematical and Scientific Machine Learning, 2020