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Sitan Chen
Sitan Chen
Assistant Professor of Computer Science, Harvard University
在 seas.harvard.edu 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
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
4172022
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
1962022
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
1062022
Linear programming bounds for randomly sampling colorings
S Chen, A Moitra
arXiv preprint arXiv:1804.03156, 2018
69*2018
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, 2024
562024
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
522023
Learning to predict arbitrary quantum processes
HY Huang, S Chen, J Preskill
PRX Quantum 4 (4), 040337, 2023
50*2023
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
502020
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
492020
The complexity of NISQ
S Chen, J Cotler, HY Huang, J Li
arXiv preprint arXiv:2210.07234, 2022
412022
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
402022
Algorithmic foundations for the diffraction limit
S Chen, A Moitra
Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing, 2020
382020
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
37*2023
Learning Polynomials of Few Relevant Dimensions
S Chen, R Meka
Proceedings of the 33rd Annual Conference on Learning Theory, 2020
362020
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
332022
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
31*2020
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
302019
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
282022
Learning mixtures of gaussians using the DDPM objective
K Shah, S Chen, A Klivans
Advances in Neural Information Processing Systems 36, 19636-19649, 2023
222023
Efficiently learning structured distributions from untrusted batches
S Chen, J Li, A Moitra
Proceedings of the 52nd Annual ACM SIGACT Symposium on Theory of Computing …, 2020
202020
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