| Quantum advantage in learning from experiments HY Huang, M Broughton, J Cotler, S Chen, J Li, M Mohseni, H Neven, ... Science 376 (6598), 1182-1186, 2022 | 539 | 2022 |
| Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions S Chen, S Chewi, J Li, Y Li, A Salim, AR Zhang arXiv preprint arXiv:2209.11215, 2022 | 290 | 2022 |
| Exponential separations between learning with and without quantum memory S Chen, J Cotler, HY Huang, J Li 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 138 | 2022 |
| The probability flow ode is provably fast S Chen, S Chewi, H Lee, Y Li, J Lu, A Salim Advances in Neural Information Processing Systems 36, 2023 | 92 | 2023 |
| Learning to predict arbitrary quantum processes HY Huang, S Chen, J Preskill PRX Quantum 4 (4), 040337, 2023 | 86 | 2023 |
| The complexity of NISQ S Chen, J Cotler, HY Huang, J Li arXiv preprint arXiv:2210.07234, 2022 | 77 | 2022 |
| Linear programming bounds for randomly sampling colorings S Chen, A Moitra arXiv preprint arXiv:1804.03156, 2018 | 76* | 2018 |
| Restoration-degradation beyond linear diffusions: A non-asymptotic analysis for ddim-type samplers S Chen, G Daras, A Dimakis International Conference on Machine Learning, 4462-4484, 2023 | 73 | 2023 |
| Entanglement is Necessary for Optimal Quantum Property Testing S Bubeck, S Chen, J Li Proceedings of the 61st Annual IEEE Symposium on Foundations of Computer Science, 2020 | 58 | 2020 |
| Learning mixtures of linear regressions in subexponential time via fourier moments S Chen, J Li, Z Song Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020 | 56 | 2020 |
| When does adaptivity help for quantum state learning? S Chen, B Huang, J Li, A Liu, M Sellke 2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS …, 2023 | 50* | 2023 |
| Learning deep relu networks is fixed-parameter tractable S Chen, AR Klivans, R Meka 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 45 | 2022 |
| Learning Polynomials of Few Relevant Dimensions S Chen, R Meka Proceedings of the 33rd Annual Conference on Learning Theory, 2020 | 43 | 2020 |
| Algorithmic foundations for the diffraction limit S Chen, A Moitra Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2020 | 39 | 2020 |
| Learning mixtures of gaussians using the DDPM objective K Shah, S Chen, A Klivans Advances in Neural Information Processing Systems 36, 19636-19649, 2023 | 38 | 2023 |
| Classification under misspecification: Halfspaces, generalized linear models, and evolvability S Chen, F Koehler, A Moitra, M Yau Advances in Neural Information Processing Systems 33, 8391-8403, 2020 | 38* | 2020 |
| Online and distribution-free robustness: Regression and contextual bandits with huber contamination S Chen, F Koehler, A Moitra, M Yau 2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 37 | 2022 |
| Hardness of noise-free learning for two-hidden-layer neural networks S Chen, A Gollakota, A Klivans, R Meka Advances in Neural Information Processing Systems 35, 10709-10724, 2022 | 34 | 2022 |
| Beyond the low-degree algorithm: mixtures of subcubes and their applications S Chen, A Moitra Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing …, 2019 | 33 | 2019 |
| Tight bounds for quantum state certification with incoherent measurements S Chen, J Li, B Huang, A Liu 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 28 | 2022 |
Jerry LiUniversity of Washington在 cs.washington.edu 的电子邮件经过验证
