| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 924 | 2023 |
| On the theory of transfer learning: The importance of task diversity N Tripuraneni, M Jordan, C Jin Advances in neural information processing systems 33, 7852-7862, 2020 | 216 | 2020 |
| Provable meta-learning of linear representations N Tripuraneni, C Jin, M Jordan International Conference on Machine Learning, 10434-10443, 2021 | 188 | 2021 |
| Stochastic cubic regularization for fast nonconvex optimization N Tripuraneni, M Stern, C Jin, J Regier, MI Jordan Advances in neural information processing systems 31, 2018 | 185 | 2018 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 156 | 2024 |
| Averaging stochastic gradient descent on Riemannian manifolds N Tripuraneni, N Flammarion, F Bach, MI Jordan Conference On Learning Theory, 650-687, 2018 | 113 | 2018 |
| Bulk viscosity and cavitation in boost-invariant hydrodynamic expansion K Rajagopal, N Tripuraneni Journal of High Energy Physics 2010 (3), 1-28, 2010 | 70 | 2010 |
| Overparameterization improves robustness to covariate shift in high dimensions N Tripuraneni, B Adlam, J Pennington Advances in Neural Information Processing Systems 34, 13883-13897, 2021 | 47 | 2021 |
| Algorithms for heavy-tailed statistics: Regression, covariance estimation, and beyond Y Cherapanamjeri, SB Hopkins, T Kathuria, P Raghavendra, ... Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020 | 45 | 2020 |
| Magnetic hamiltonian monte carlo N Tripuraneni, M Rowland, Z Ghahramani, R Turner International Conference on Machine Learning, 3453-3461, 2017 | 44 | 2017 |
| Rao-Blackwellized stochastic gradients for discrete distributions R Liu, J Regier, N Tripuraneni, M Jordan, J Mcauliffe International Conference on Machine Learning, 4023-4031, 2019 | 40 | 2019 |
| Optimal robust linear regression in nearly linear time Y Cherapanamjeri, E Aras, N Tripuraneni, MI Jordan, N Flammarion, ... arXiv preprint arXiv:2007.08137, 2020 | 37 | 2020 |
| Lost relatives of the Gumbel trick M Balog, N Tripuraneni, Z Ghahramani, A Weller International Conference on Machine Learning, 371-379, 2017 | 32 | 2017 |
| Covariate shift in high-dimensional random feature regression N Tripuraneni, B Adlam, J Pennington arXiv preprint arXiv:2111.08234, 2021 | 26 | 2021 |
| Particle Gibbs for infinite hidden Markov models N Tripuraneni, SS Gu, H Ge, Z Ghahramani Advances in Neural Information Processing Systems 28, 2015 | 26 | 2015 |
| Optimal mean estimation without a variance Y Cherapanamjeri, N Tripuraneni, P Bartlett, M Jordan Conference on Learning Theory, 356-357, 2022 | 25 | 2022 |
| Quantitative criticism of literary relationships JP Dexter, T Katz, N Tripuraneni, T Dasgupta, A Kannan, JA Brofos, ... Proceedings of the National Academy of Sciences 114 (16), E3195-E3204, 2017 | 25 | 2017 |
| Evaluating Stream Filtering for Entity Profile Updates for TREC 2013. JR Frank, SJ Bauer, M Kleiman-Weiner, DA Roberts, N Tripuraneni, ... TREC 2013, 21, 2013 | 25 | 2013 |
| Pretraining data mixtures enable narrow model selection capabilities in transformer models S Yadlowsky, L Doshi, N Tripuraneni arXiv preprint arXiv:2311.00871, 2023 | 21 | 2023 |
| Parallelizing contextual bandits J Chan, A Pacchiano, N Tripuraneni, YS Song, P Bartlett, MI Jordan arXiv preprint arXiv:2105.10590, 2021 | 12 | 2021 |
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC Berkeley在 cs.berkeley.edu 的电子邮件经过验证
