| Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit S Mei, T Misiakiewicz, A Montanari Conference on learning theory, 2388-2464, 2019 | 319 | 2019 |
| Linearized two-layers neural networks in high dimension B Ghorbani, S Mei, T Misiakiewicz, A Montanari | 271 | 2021 |
| When do neural networks outperform kernel methods? B Ghorbani, S Mei, T Misiakiewicz, A Montanari Advances in Neural Information Processing Systems 33, 14820-14830, 2020 | 211 | 2020 |
| Limitations of lazy training of two-layers neural network B Ghorbani, S Mei, T Misiakiewicz, A Montanari Advances in Neural Information Processing Systems 32, 2019 | 158 | 2019 |
| Generalization error of random feature and kernel methods: hypercontractivity and kernel matrix concentration S Mei, T Misiakiewicz, A Montanari Applied and Computational Harmonic Analysis 59, 3-84, 2022 | 145 | 2022 |
| The merged-staircase property: a necessary and nearly sufficient condition for sgd learning of sparse functions on two-layer neural networks E Abbe, EB Adsera, T Misiakiewicz Conference on Learning Theory, 4782-4887, 2022 | 122 | 2022 |
| Learning with invariances in random features and kernel models S Mei, T Misiakiewicz, A Montanari Conference on Learning Theory, 3351-3418, 2021 | 86 | 2021 |
| Sgd learning on neural networks: leap complexity and saddle-to-saddle dynamics E Abbe, EB Adsera, T Misiakiewicz The Thirty Sixth Annual Conference on Learning Theory, 2552-2623, 2023 | 81 | 2023 |
| Solving SDPs for synchronization and MaxCut problems via the Grothendieck inequality S Mei, T Misiakiewicz, A Montanari, RI Oliveira Conference on learning theory, 1476-1515, 2017 | 80 | 2017 |
| Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression L Xiao, H Hu, T Misiakiewicz, YM Lu, J Pennington Journal of Statistical Mechanics: Theory and Experiment 2023 (11), 114005, 2023 | 52* | 2023 |
| Spectrum of inner-product kernel matrices in the polynomial regime and multiple descent phenomenon in kernel ridge regression T Misiakiewicz arXiv preprint arXiv:2204.10425, 2022 | 45 | 2022 |
| Learning with convolution and pooling operations in kernel methods T Misiakiewicz, S Mei Advances in Neural Information Processing Systems 35, 29014-29025, 2022 | 25 | 2022 |
| Asymptotics of random feature regression beyond the linear scaling regime H Hu, YM Lu, T Misiakiewicz arXiv preprint arXiv:2403.08160, 2024 | 13 | 2024 |
| Six lectures on linearized neural networks T Misiakiewicz, A Montanari Journal of Statistical Mechanics: Theory and Experiment 2024 (10), 104006, 2024 | 11 | 2024 |
| Discussion of:“Nonparametric regression using deep neural networks with ReLU activation function” B Ghorbani, S Mei, T Misiakiewicz, A Montanari | 11 | 2020 |
| Efficient reconstruction of transmission probabilities in a spreading process from partial observations AY Lokhov, T Misiakiewicz arXiv preprint arXiv:1509.06893, 2015 | 9 | 2015 |
| Minimum complexity interpolation in random features models M Celentano, T Misiakiewicz, A Montanari arXiv preprint arXiv:2103.15996, 2021 | 8 | 2021 |
| A non-asymptotic theory of Kernel Ridge Regression: deterministic equivalents, test error, and GCV estimator T Misiakiewicz, B Saeed arXiv preprint arXiv:2403.08938, 2024 | 7 | 2024 |
| Dimension-free deterministic equivalents for random feature regression L Defilippis, B Loureiro, T Misiakiewicz arXiv preprint arXiv:2405.15699, 2024 | 5 | 2024 |
| On the complexity of learning sparse functions with statistical and gradient queries N Joshi, T Misiakiewicz, N Srebro arXiv preprint arXiv:2407.05622, 2024 | 2 | 2024 |
Andrea MontanariProfessor of Statistics and Mathematics, Stanford UniversityE-mail confirmado em stanford.edu
