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2020 – today
- 2024
[c32]Sitan Chen, Shyam Narayanan:
A faster and simpler algorithm for learning shallow networks. COLT 2024: 981-994- [c31]Marvin Li, Sitan Chen:
Critical windows: non-asymptotic theory for feature emergence in diffusion models. ICML 2024 - [c30]Sitan Chen
, Jerry Li, Allen Liu:
An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. STOC 2024: 1331-1342 - [i48]Sitan Chen, Yuanzhi Li:
Provably learning a multi-head attention layer. CoRR abs/2402.04084 (2024) - [i47]Sitan Chen, Jerry Li, Allen Liu:
An optimal tradeoff between entanglement and copy complexity for state tomography. CoRR abs/2402.16353 (2024) - [i46]Marvin Li, Sitan Chen:
Critical windows: non-asymptotic theory for feature emergence in diffusion models. CoRR abs/2403.01633 (2024) - [i45]Sitan Chen, Vasilis Kontonis, Kulin Shah:
Learning general Gaussian mixtures with efficient score matching. CoRR abs/2404.18893 (2024) - [i44]Sitan Chen, Weiyuan Gong, Qi Ye:
Optimal tradeoffs for estimating Pauli observables. CoRR abs/2404.19105 (2024) - [i43]Shivam Gupta, Linda Cai, Sitan Chen:
Faster Diffusion-based Sampling with Randomized Midpoints: Sequential and Parallel. CoRR abs/2406.00924 (2024) - [i42]Sitan Chen, Jerry Li, Allen Liu:
Optimal high-precision shadow estimation. CoRR abs/2407.13874 (2024) - [i41]Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang:
Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation. CoRR abs/2408.06967 (2024) - [i40]Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li:
Predicting quantum channels over general product distributions. CoRR abs/2409.03684 (2024) - [i39]Aayush Karan, Kulin Shah, Sitan Chen, Yonina C. Eldar:
Unrolled denoising networks provably learn optimal Bayesian inference. CoRR abs/2409.12947 (2024) - [i38]Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu:
What does guidance do? A fine-grained analysis in a simple setting. CoRR abs/2409.13074 (2024) - 2023
- [c29]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. COLT 2023: 5580-5614 - [c28]Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke:
When Does Adaptivity Help for Quantum State Learning? FOCS 2023: 391-404 - [c27]Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. ICLR 2023 - [c26]Sitan Chen, Giannis Daras, Alex Dimakis:
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. ICML 2023: 4462-4484 - [c25]Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim:
The probability flow ODE is provably fast. NeurIPS 2023 - [c24]Kulin Shah, Sitan Chen, Adam R. Klivans:
Learning Mixtures of Gaussians Using the DDPM Objective. NeurIPS 2023 - [c23]Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang:
Learning Polynomial Transformations via Generalized Tensor Decompositions. STOC 2023: 1671-1684 - [i37]Sitan Chen, Giannis Daras, Alexandros G. Dimakis:
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers. CoRR abs/2303.03384 (2023) - [i36]Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka:
Learning Narrow One-Hidden-Layer ReLU Networks. CoRR abs/2304.10524 (2023) - [i35]Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim:
The probability flow ODE is provably fast. CoRR abs/2305.11798 (2023) - [i34]Kulin Shah, Sitan Chen, Adam R. Klivans:
Learning Mixtures of Gaussians Using the DDPM Objective. CoRR abs/2307.01178 (2023) - [i33]Sitan Chen, Shyam Narayanan:
A faster and simpler algorithm for learning shallow networks. CoRR abs/2307.12496 (2023) - [i32]Sitan Chen, Weiyuan Gong:
Futility and utility of a few ancillas for Pauli channel learning. CoRR abs/2309.14326 (2023) - 2022
- [c22]Sitan Chen, Jerry Li, Ryan O'Donnell:
Toward Instance-Optimal State Certification With Incoherent Measurements. COLT 2022: 2541-2596 - [c21]Sitan Chen, Jerry Li, Brice Huang, Allen Liu:
Tight Bounds for Quantum State Certification with Incoherent Measurements. FOCS 2022: 1205-1213 - [c20]Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka:
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. ICLR 2022 - [c19]Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang:
Symmetric Sparse Boolean Matrix Factorization and Applications. ITCS 2022: 46:1-46:25 - [c18]Sitan Chen, Jerry Li, Yuanzhi Li:
Learning (Very) Simple Generative Models Is Hard. NeurIPS 2022 - [c17]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS 2022 - [c16]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Kalman filtering with adversarial corruptions. STOC 2022: 832-845 - [i31]Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka:
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. CoRR abs/2201.07206 (2022) - [i30]Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka:
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR abs/2202.05258 (2022) - [i29]Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang:
Learning Polynomial Transformations. CoRR abs/2204.04209 (2022) - [i28]Sitan Chen, Brice Huang, Jerry Li, Allen Liu:
Tight Bounds for Quantum State Certification with Incoherent Measurements. CoRR abs/2204.07155 (2022) - [i27]Sitan Chen, Jerry Li, Yuanzhi Li:
Learning (Very) Simple Generative Models Is Hard. CoRR abs/2205.16003 (2022) - [i26]Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke:
Tight Bounds for State Tomography with Incoherent Measurements. CoRR abs/2206.05265 (2022) - [i25]Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang:
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. CoRR abs/2209.11215 (2022) - [i24]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
The Complexity of NISQ. CoRR abs/2210.07234 (2022) - [i23]Hsin-Yuan Huang, Sitan Chen, John Preskill:
Learning to predict arbitrary quantum processes. CoRR abs/2210.14894 (2022) - 2021
- [b1]Sitan Chen:
Rethinking Algorithm Design for Modern Challenges in Data Science. Massachusetts Institute of Technology, USA, 2021 - [c15]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
Exponential Separations Between Learning With and Without Quantum Memory. FOCS 2021: 574-585 - [c14]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS 2021: 684-695 - [c13]Sitan Chen, Adam R. Klivans, Raghu Meka:
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS 2021: 696-707 - [c12]Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo:
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. ICLR 2021 - [c11]Sitan Chen, Adam R. Klivans, Raghu Meka:
Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS 2021: 24087-24098 - [c10]Sitan Chen, Ankur Moitra:
Algorithmic foundations for the diffraction limit. STOC 2021: 490-503 - [i22]Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang:
Symmetric Boolean Factor Analysis with Applications to InstaHide. CoRR abs/2102.01570 (2021) - [i21]Sitan Chen, Jerry Li, Ryan O'Donnell:
Toward Instance-Optimal State Certification With Incoherent Measurements. CoRR abs/2102.13098 (2021) - [i20]Sitan Chen, Adam R. Klivans, Raghu Meka:
Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR abs/2111.04727 (2021) - [i19]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
A Hierarchy for Replica Quantum Advantage. CoRR abs/2111.05874 (2021) - [i18]Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li:
Exponential separations between learning with and without quantum memory. CoRR abs/2111.05881 (2021) - [i17]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Kalman Filtering with Adversarial Corruptions. CoRR abs/2111.06395 (2021) - [i16]Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean:
Quantum advantage in learning from experiments. CoRR abs/2112.00778 (2021) - 2020
- [c9]Sitan Chen, Raghu Meka:
Learning Polynomials in Few Relevant Dimensions. COLT 2020: 1161-1227 - [c8]Sébastien Bubeck, Sitan Chen, Jerry Li:
Entanglement is Necessary for Optimal Quantum Property Testing. FOCS 2020: 692-703 - [c7]Sitan Chen, Jerry Li, Ankur Moitra:
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. NeurIPS 2020 - [c6]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS 2020 - [c5]Sitan Chen, Jerry Li, Zhao Song:
Learning mixtures of linear regressions in subexponential time via Fourier moments. STOC 2020: 587-600 - [c4]Sitan Chen, Jerry Li, Ankur Moitra:
Efficiently learning structured distributions from untrusted batches. STOC 2020: 960-973 - [i15]Sitan Chen, Jerry Li, Ankur Moitra:
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. CoRR abs/2002.10435 (2020) - [i14]Sitan Chen, Ankur Moitra:
Algorithmic Foundations for the Diffraction Limit. CoRR abs/2004.07659 (2020) - [i13]Sébastien Bubeck, Sitan Chen, Jerry Li:
Entanglement is Necessary for Optimal Quantum Property Testing. CoRR abs/2004.07869 (2020) - [i12]Sitan Chen, Raghu Meka:
Learning Polynomials of Few Relevant Dimensions. CoRR abs/2004.13748 (2020) - [i11]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR abs/2006.04787 (2020) - [i10]Sitan Chen, Adam R. Klivans, Raghu Meka:
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR abs/2009.13512 (2020) - [i9]Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau:
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR abs/2010.04157 (2020) - [i8]Sitan Chen, Zhao Song, Danyang Zhuo:
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. CoRR abs/2011.11181 (2020)
2010 – 2019
- 2019
- [c3]Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle:
Improved Bounds for Randomly Sampling Colorings via Linear Programming. SODA 2019: 2216-2234 - [c2]Sitan Chen, Ankur Moitra:
Beyond the low-degree algorithm: mixtures of subcubes and their applications. STOC 2019: 869-880 - [i7]Sitan Chen, Jerry Li, Ankur Moitra:
Efficiently Learning Structured Distributions from Untrusted Batches. CoRR abs/1911.02035 (2019) - [i6]Sitan Chen, Jerry Li, Zhao Song:
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments. CoRR abs/1912.07629 (2019) - 2018
- [i5]Sitan Chen, Ankur Moitra:
Learning Mixtures of Product Distributions via Higher Multilinear Moments. CoRR abs/1803.06521 (2018) - [i4]Sitan Chen, Ankur Moitra:
Linear Programming Bounds for Randomly Sampling Colorings. CoRR abs/1804.03156 (2018) - [i3]Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle:
Improved Bounds for Randomly Sampling Colorings via Linear Programming. CoRR abs/1810.12980 (2018) - 2016
- [c1]Sitan Chen:
Basis collapse for holographic algorithms over all domain sizes. STOC 2016: 776-789 - 2015
- [i2]Sitan Chen, Thomas Steinke, Salil P. Vadhan:
Pseudorandomness for Read-Once, Constant-Depth Circuits. CoRR abs/1504.04675 (2015) - [i1]Sitan Chen:
Basis Collapse for Holographic Algorithms Over All Domain Sizes. CoRR abs/1511.00778 (2015) - 2014
- [j1]Sitan Chen:
Cellular Automata to More Efficiently Compute the Collatz Map. Int. J. Unconv. Comput. 10(1-2): 1-18 (2014)
Coauthor Index
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