Hugo Cui

According to our database, Hugo Cui authored at least 16 papers between 2021 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

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

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

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

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

Hugo Cui, Luca Saglietti, Lenka Zdeborová

Mach. Learn. Sci. Technol., 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