Hugo Cui - RankMe

32 papers A* 10A 1Journal 19Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Asymptotic Theory of Iterated Empirical Risk Minimization, with Applications to Active Learning. CoRR Hugo Cui, Yue M. Lu
2025	A	`conf`	A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities. AISTATS Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, Bruno Loureiro
2025	J	`jnl`	A precise asymptotic analysis of learning diffusion models: theory and insights. CoRR Hugo Cui, Cengiz Pehlevan, Yue M. Lu
2025	J	`jnl`	Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds. CoRR Emanuele Troiani, Hugo Cui, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
2025	A*	`conf`	Fundamental limits of learning in sequence multi-index models and deep attention networks: high-dimensional asymptotics and sharp thresholds. ICML Emanuele Troiani, Hugo Cui, Yatin Dandi, Florent Krzakala, Lenka Zdeborová
2025	J	`jnl`	High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification. CoRR Nicholas Barnfield, Hugo Cui, Yue M. Lu
2024	A*	`conf`	A Phase Transition between Positional and Semantic Learning in a Solvable Model of Dot-Product Attention. NeurIPS Hugo Cui, Freya Behrens, Florent Krzakala, Lenka Zdeborová
2024	J	`jnl`	A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities. CoRR Yatin Dandi, Luca Pesce, Hugo Cui, Florent Krzakala, Yue M. Lu, Bruno Loureiro
2024	J	`jnl`	A phase transition between positional and semantic learning in a solvable model of dot-product attention. CoRR Hugo Cui, Freya Behrens, Florent Krzakala, Lenka Zdeborová
2024	A*	`conf`	Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. ICLR Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
2024	A*	`conf`	Asymptotics of Learning with Deep Structured (Random) Features. ICML Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro
2024	J	`jnl`	Asymptotics of Learning with Deep Structured (Random) Features. CoRR Dominik Schröder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro
2024	A*	`conf`	Asymptotics of feature learning in two-layer networks after one gradient-step. ICML Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
2024	J	`jnl`	Asymptotics of feature learning in two-layer networks after one gradient-step. CoRR Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborová, Bruno Loureiro
2024	J	`jnl`	High-dimensional learning of narrow neural networks. CoRR Hugo Cui
2024	—	`—`	Topics in statistical physics of high-dimensional machine learning. Hugo Cui
2023	J	`jnl`	Analysis of learning a flow-based generative model from limited sample complexity. CoRR Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborová
2023	A*	`conf`	Bayes-optimal Learning of Deep Random Networks of Extensive-width. ICML Hugo Cui, Florent Krzakala, Lenka Zdeborová
2023	A*	`conf`	Deterministic equivalent and error universality of deep random features learning. ICML Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro
2023	J	`jnl`	Deterministic equivalent and error universality of deep random features learning. CoRR Dominik Schröder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro
2023	J	`jnl`	Error scaling laws for kernel classification under source and capacity conditions. Mach. Learn. Sci. Technol. Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
2023	A*	`conf`	High-dimensional Asymptotics of Denoising Autoencoders. NeurIPS Hugo Cui, Lenka Zdeborová
2023	J	`jnl`	High-dimensional Asymptotics of Denoising Autoencoders. CoRR Hugo Cui, Lenka Zdeborová
2023	J	`jnl`	Optimal Learning of Deep Random Networks of Extensive-width. CoRR Hugo Cui, Florent Krzakala, Lenka Zdeborová
2022	J	`jnl`	Error Rates for Kernel Classification under Source and Capacity Conditions. CoRR Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
2021	J	`jnl`	Capturing the learning curves of generic features maps for realistic data sets with a teacher-student model. CoRR Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová
2021	A*	`conf`	Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime. NeurIPS Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
2021	J	`jnl`	Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime. CoRR Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová
2021	J	`jnl`	Large deviations in the perceptron model and consequences for active learning. Mach. Learn. Sci. Technol. Hugo Cui, Luca Saglietti, Lenka Zdeborová
2021	A*	`conf`	Learning curves of generic features maps for realistic datasets with a teacher-student model. NeurIPS Bruno Loureiro, Cédric Gerbelot, Hugo Cui, Sebastian Goldt, Florent Krzakala, Marc Mézard, Lenka Zdeborová
2020	—	`conf`	Large deviations for the perceptron model and consequences for active learning. MSML Hugo Cui, Luca Saglietti, Lenka Zdeborová
2019	J	`jnl`	Large deviations for the perceptron model and consequences for active learning. CoRR Hugo Cui, Luca Saglietti, Lenka Zdeborová