Sitan Chen

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 76

Top Topics

Asymptotic Analysis

Diffusion Models

Top Venues

Publications

Shivam Gupta, Linda Cai, Sitan Chen
Faster Diffusion-based Sampling with Randomized Midpoints: Sequential and Parallel. CoRR (2024)
Sitan Chen, Yuanzhi Li
Provably learning a multi-head attention layer. CoRR (2024)
Sitan Chen, Jerry Li, Allen Liu
An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. STOC (2024)
Sitan Chen, Jerry Li, Allen Liu
Optimal high-precision shadow estimation. CoRR (2024)
Sitan Chen, Vasilis Kontonis, Kulin Shah
Learning general Gaussian mixtures with efficient score matching. CoRR (2024)
Sitan Chen, Shyam Narayanan
A faster and simpler algorithm for learning shallow networks. COLT (2024)
Marvin Li, Sitan Chen
Critical windows: non-asymptotic theory for feature emergence in diffusion models. CoRR (2024)
Sitan Chen, Weiyuan Gong, Qi Ye
Optimal tradeoffs for estimating Pauli observables. CoRR (2024)
Sitan Chen, Jerry Li, Allen Liu
An optimal tradeoff between entanglement and copy complexity for state tomography. CoRR (2024)
Sitan Chen, Weiyuan Gong
Futility and utility of a few ancillas for Pauli channel learning. CoRR (2023)
Kulin Shah, Sitan Chen, Adam R. Klivans
Learning Mixtures of Gaussians Using the DDPM Objective. CoRR (2023)
Sitan Chen, Giannis Daras, Alexandros G. Dimakis
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers. CoRR (2023)
Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
Learning Narrow One-Hidden-Layer ReLU Networks. CoRR (2023)
Sitan Chen, Shyam Narayanan
A faster and simpler algorithm for learning shallow networks. CoRR (2023)
Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
Learning Narrow One-Hidden-Layer ReLU Networks. COLT (2023)
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
The probability flow ODE is provably fast. CoRR (2023)
Kulin Shah, Sitan Chen, Adam R. Klivans
Learning Mixtures of Gaussians Using the DDPM Objective. NeurIPS (2023)
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
The probability flow ODE is provably fast. NeurIPS (2023)
Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
When Does Adaptivity Help for Quantum State Learning? FOCS (2023)
Sitan Chen, Giannis Daras, Alex Dimakis
Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. ICML (2023)
Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
Learning Polynomial Transformations via Generalized Tensor Decompositions. STOC (2023)
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)
Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. CoRR (2022)
Sitan Chen, Brice Huang, Jerry Li, Allen Liu
Tight Bounds for Quantum State Certification with Incoherent Measurements. CoRR (2022)
Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
Symmetric Sparse Boolean Matrix Factorization and Applications. ITCS (2022)
Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. ICLR (2022)
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR (2022)
Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
Tight Bounds for State Tomography with Incoherent Measurements. CoRR (2022)
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
The Complexity of NISQ. CoRR (2022)
Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS (2022)
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 (2022)
Sitan Chen, Jerry Li, Ryan O'Donnell
Toward Instance-Optimal State Certification With Incoherent Measurements. COLT (2022)
Hsin-Yuan Huang, Sitan Chen, John Preskill
Learning to predict arbitrary quantum processes. CoRR (2022)
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
Exponential Separations Between Learning With and Without Quantum Memory. FOCS (2022)
Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
Learning Polynomial Transformations. CoRR (2022)
Sitan Chen, Jerry Li, Yuanzhi Li
Learning (Very) Simple Generative Models Is Hard. CoRR (2022)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS (2022)
Sitan Chen, Jerry Li, Brice Huang, Allen Liu
Tight Bounds for Quantum State Certification with Incoherent Measurements. FOCS (2022)
Sitan Chen, Adam R. Klivans, Raghu Meka
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS (2022)
Sitan Chen, Jerry Li, Yuanzhi Li
Learning (Very) Simple Generative Models Is Hard. NeurIPS (2022)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Kalman filtering with adversarial corruptions. STOC (2022)
Sitan Chen, Adam R. Klivans, Raghu Meka
Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR (2021)
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
Exponential separations between learning with and without quantum memory. CoRR (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS (2021)
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
Exponential Separations Between Learning With and Without Quantum Memory. FOCS (2021)
Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. ICLR (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Kalman Filtering with Adversarial Corruptions. CoRR (2021)
Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
Symmetric Boolean Factor Analysis with Applications to InstaHide. CoRR (2021)
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 (2021)
Sitan Chen, Jerry Li, Ryan O'Donnell
Toward Instance-Optimal State Certification With Incoherent Measurements. CoRR (2021)
Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
A Hierarchy for Replica Quantum Advantage. CoRR (2021)
Sitan Chen, Ankur Moitra
Algorithmic foundations for the diffraction limit. STOC (2021)
Sitan Chen, Adam R. Klivans, Raghu Meka
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS (2021)
Sitan Chen, Adam R. Klivans, Raghu Meka
Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS (2021)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR (2020)
Sitan Chen, Jerry Li, Ankur Moitra
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. NeurIPS (2020)
Sitan Chen, Raghu Meka
Learning Polynomials of Few Relevant Dimensions. CoRR (2020)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS (2020)
Sitan Chen, Adam R. Klivans, Raghu Meka
Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR (2020)
Sébastien Bubeck, Sitan Chen, Jerry Li
Entanglement is Necessary for Optimal Quantum Property Testing. FOCS (2020)
Sitan Chen, Jerry Li, Ankur Moitra
Learning Structured Distributions From Untrusted Batches: Faster and Simpler. CoRR (2020)
Sitan Chen, Raghu Meka
Learning Polynomials in Few Relevant Dimensions. COLT (2020)
Sitan Chen, Jerry Li, Ankur Moitra
Efficiently learning structured distributions from untrusted batches. STOC (2020)
Sitan Chen, Jerry Li, Zhao Song
Learning mixtures of linear regressions in subexponential time via Fourier moments. STOC (2020)
Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR (2020)
Sitan Chen, Zhao Song, Danyang Zhuo
On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. CoRR (2020)
Sitan Chen, Ankur Moitra
Algorithmic Foundations for the Diffraction Limit. CoRR (2020)
Sébastien Bubeck, Sitan Chen, Jerry Li
Entanglement is Necessary for Optimal Quantum Property Testing. CoRR (2020)
Sitan Chen, Jerry Li, Zhao Song
Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments. CoRR (2019)
Sitan Chen, Jerry Li, Ankur Moitra
Efficiently Learning Structured Distributions from Untrusted Batches. CoRR (2019)
Sitan Chen, Ankur Moitra
Beyond the low-degree algorithm: mixtures of subcubes and their applications. STOC (2019)
Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
Improved Bounds for Randomly Sampling Colorings via Linear Programming. SODA (2019)
Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
Improved Bounds for Randomly Sampling Colorings via Linear Programming. CoRR (2018)
Sitan Chen, Ankur Moitra
Learning Mixtures of Product Distributions via Higher Multilinear Moments. CoRR (2018)
Sitan Chen, Ankur Moitra
Linear Programming Bounds for Randomly Sampling Colorings. CoRR (2018)
Sitan Chen
Basis collapse for holographic algorithms over all domain sizes. STOC (2016)
Sitan Chen, Thomas Steinke, Salil P. Vadhan
Pseudorandomness for Read-Once, Constant-Depth Circuits. CoRR (2015)
Sitan Chen
Basis Collapse for Holographic Algorithms Over All Domain Sizes. CoRR (2015)
Sitan Chen
Cellular Automata to More Efficiently Compute the Collatz Map. Int. J. Unconv. Comput. 10 (1-2) (2014)