| 2025 | AISTATS | 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 |
| 2025 | ICML | 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 Zdeborov |
| 2024 | ICLR | Analysis of Learning a Flow-based Generative Model from Limited Sample Complexity. | Hugo Cui, Florent Krzakala, Eric Vanden-Eijnden, Lenka Zdeborov |
| 2024 | ICML | Asymptotics of feature learning in two-layer networks after one gradient-step. | Hugo Cui, Luca Pesce, Yatin Dandi, Florent Krzakala, Yue M. Lu, Lenka Zdeborov, Bruno Loureiro |
| 2024 | ICML | Asymptotics of Learning with Deep Structured (Random) Features. | Dominik Schrder, Daniil Dmitriev, Hugo Cui, Bruno Loureiro |
| 2023 | ICML | Bayes-optimal Learning of Deep Random Networks of Extensive-width. | Hugo Cui, Florent Krzakala, Lenka Zdeborov |
| 2023 | ICML | Deterministic equivalent and error universality of deep random features learning. | Dominik Schrder, Hugo Cui, Daniil Dmitriev, Bruno Loureiro |