| How two-layer neural networks learn, one (giant) step at a time Y Dandi, F Krzakala, B Loureiro, L Pesce, L Stephan arXiv preprint arXiv:2305.18270, 2023 | 39 | 2023 |
| Implicit gradient alignment in distributed and federated learning Y Dandi, L Barba, M Jaggi Proceedings of the AAAI Conference on Artificial Intelligence 36 (6), 6454-6462, 2022 | 26 | 2022 |
| Universality laws for gaussian mixtures in generalized linear models Y Dandi, L Stephan, F Krzakala, B Loureiro, L Zdeborová Advances in Neural Information Processing Systems 36, 2024 | 25 | 2024 |
| The benefits of reusing batches for gradient descent in two-layer networks: Breaking the curse of information and leap exponents Y Dandi, E Troiani, L Arnaboldi, L Pesce, L Zdeborová, F Krzakala arXiv preprint arXiv:2402.03220, 2024 | 20 | 2024 |
| Data-heterogeneity-aware mixing for decentralized learning Y Dandi, A Koloskova, M Jaggi, SU Stich arXiv preprint arXiv:2204.06477, 2022 | 19 | 2022 |
| Sampling with flows, diffusion, and autoregressive neural networks from a spin-glass perspective D Ghio, Y Dandi, F Krzakala, L Zdeborová Proceedings of the National Academy of Sciences 121 (27), e2311810121, 2024 | 18 | 2024 |
| Asymptotics of feature learning in two-layer networks after one gradient-step H Cui, L Pesce, Y Dandi, F Krzakala, YM Lu, L Zdeborová, B Loureiro arXiv preprint arXiv:2402.04980, 2024 | 15 | 2024 |
| Jointly trained image and video generation using residual vectors Y Dandi, A Das, S Singhal, V Namboodiri, P Rai Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2020 | 9 | 2020 |
| Repetita iuvant: Data repetition allows sgd to learn high-dimensional multi-index functions L Arnaboldi, Y Dandi, F Krzakala, L Pesce, L Stephan arXiv preprint arXiv:2405.15459, 2024 | 7 | 2024 |
| Generalized Adversarially Learned Inference Y Dandi, H Bharadhwaj, A Kumar, P Rai AAAI, 2021, 2020 | 6 | 2020 |
| Maximally-stable local optima in random graphs and spin glasses: Phase transitions and universality Y Dandi, D Gamarnik, L Zdeborová arXiv preprint arXiv:2305.03591, 2023 | 5 | 2023 |
| Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis Y Dandi, A Jacot arXiv preprint arXiv:2111.03972, 2021 | 3 | 2021 |
| Online Learning and Information Exponents: On The Importance of Batch size, and Time/Complexity Tradeoffs L Arnaboldi, Y Dandi, F Krzakala, B Loureiro, L Pesce, L Stephan arXiv preprint arXiv:2406.02157, 2024 | 2 | 2024 |
| Model-Agnostic Learning to Meta-Learn A Devos, Y Dandi NeurIPS pre-registration workshop, 2020, 2020 | 2 | 2020 |
| Fundamental limits of weak learnability in high-dimensional multi-index models E Troiani, Y Dandi, L Defilippis, L Zdeborová, B Loureiro, F Krzakala arXiv preprint arXiv:2405.15480, 2024 | 1 | 2024 |
| Learning from setbacks: the impact of adversarial initialization on generalization performance K Ravichandran, Y Dandi, S Karp, F Mignacco NeurIPS 2023 Workshop on Mathematics of Modern Machine Learning, 0 | 1 | |
| A gentle introduction to gradient-based optimization and variational inequalities for machine learning NS Wadia, Y Dandi, MI Jordan Journal of Statistical Mechanics: Theory and Experiment 2024 (10), 104009, 2024 | | 2024 |
| A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities Y Dandi, L Pesce, H Cui, F Krzakala, YM Lu, B Loureiro arXiv preprint arXiv:2410.18938, 2024 | | 2024 |
| NeurInt: Learning to Interpolate through Neural ODEs A Bose, A Das, Y Dandi, P Rai arXiv preprint arXiv:2111.04123, 2021 | | 2021 |
| How Two-Layer Networks Learn, One (Giant) Step at a Time Y Dandi, F Krzakala, B Loureiro, L Pesce, L Stephan | | |
Florent KrzakalaÉcole polytechnique fédérale de LausanneVerified email at epfl.ch
