Cellular Automata to More Efficiently Compute the Collatz Map. - Sitan Chen (Int. J. Unconv. Comput., 2014)

Pseudorandomness for Read-Once, Constant-Depth Circuits. - Sitan Chen; Thomas Steinke 002; Salil P. Vadhan (CoRR, 2015)

Basis collapse for holographic algorithms over all domain sizes. - Sitan Chen (STOC, 2016)

Learning Mixtures of Product Distributions via Higher Multilinear Moments. - Sitan Chen; Ankur Moitra (CoRR, 2018)

Linear Programming Bounds for Randomly Sampling Colorings. - Sitan Chen; Ankur Moitra (CoRR, 2018)

Improved Bounds for Randomly Sampling Colorings via Linear Programming. - Sitan Chen; Michelle Delcourt; Ankur Moitra; Guillem Perarnau; Luke Postle (SODA, 2019)

Beyond the low-degree algorithm: mixtures of subcubes and their applications. - Sitan Chen; Ankur Moitra (STOC, 2019)

Learning Polynomials in Few Relevant Dimensions. - Sitan Chen; Raghu Meka (COLT, 2020)

Entanglement is Necessary for Optimal Quantum Property Testing. - Sébastien Bubeck; Sitan Chen; Jerry Li 001 (FOCS, 2020)

Learning Structured Distributions From Untrusted Batches: Faster and Simpler. - Sitan Chen; Jerry Li 001; Ankur Moitra (NeurIPS, 2020)

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (NeurIPS, 2020)

Efficiently learning structured distributions from untrusted batches. - Sitan Chen; Jerry Li 001; Ankur Moitra (STOC, 2020)

Learning mixtures of linear regressions in subexponential time via Fourier moments. - Sitan Chen; Jerry Li 001; Zhao Song 002 (STOC, 2020)

Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (CoRR, 2020)

On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. - Sitan Chen; Zhao Song 002; Danyang Zhuo (CoRR, 2020)

Exponential Separations Between Learning With and Without Quantum Memory. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (FOCS, 2021)

Learning Deep ReLU Networks Is Fixed-Parameter Tractable. - Sitan Chen; Adam R. Klivans; Raghu Meka (FOCS, 2021)

Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (FOCS, 2021)

On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. - Sitan Chen; Xiaoxiao Li; Zhao Song 002; Danyang Zhuo (ICLR, 2021)

Efficiently Learning One Hidden Layer ReLU Networks From Queries. - Sitan Chen; Adam R. Klivans; Raghu Meka (NeurIPS, 2021)

Algorithmic foundations for the diffraction limit. - Sitan Chen; Ankur Moitra (STOC, 2021)

Symmetric Boolean Factor Analysis with Applications to InstaHide. - Sitan Chen; Zhao Song 002; Runzhou Tao; Ruizhe Zhang 001 (CoRR, 2021)

Efficiently Learning Any One Hidden Layer ReLU Network From Queries. - Sitan Chen; Adam R. Klivans; Raghu Meka (CoRR, 2021)

A Hierarchy for Replica Quantum Advantage. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (CoRR, 2021)

Quantum advantage in learning from experiments. - Hsin-Yuan Huang; Michael Broughton; Jordan Cotler; Sitan Chen; Jerry Li 001; Masoud Mohseni; Hartmut Neven; Ryan Babbush; Richard Kueng; John Preskill; Jarrod R. McClean (CoRR, 2021)

Rethinking Algorithm Design for Modern Challenges in Data Science. - Sitan Chen (2021)

Toward Instance-Optimal State Certification With Incoherent Measurements. - Sitan Chen; Jerry Li 001; Ryan O'Donnell (COLT, 2022)

Tight Bounds for Quantum State Certification with Incoherent Measurements. - Sitan Chen; Jerry Li 001; Brice Huang; Allen Liu (FOCS, 2022)

Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Raghu Meka (ICLR, 2022)

Symmetric Sparse Boolean Matrix Factorization and Applications. - Sitan Chen; Zhao Song 002; Runzhou Tao; Ruizhe Zhang 001 (ITCS, 2022)

Learning (Very) Simple Generative Models Is Hard. - Sitan Chen; Jerry Li 001; Yuanzhi Li (NeurIPS, 2022)

Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. - Sitan Chen; Aravind Gollakota; Adam R. Klivans; Raghu Meka (NeurIPS, 2022)

Kalman filtering with adversarial corruptions. - Sitan Chen; Frederic Koehler; Ankur Moitra; Morris Yau (STOC, 2022)

Learning Polynomial Transformations. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Anru R. Zhang (CoRR, 2022)

Tight Bounds for State Tomography with Incoherent Measurements. - Sitan Chen; Brice Huang; Jerry Li 001; Allen Liu; Mark Sellke (CoRR, 2022)

The Complexity of NISQ. - Sitan Chen; Jordan Cotler; Hsin-Yuan Huang; Jerry Li 001 (CoRR, 2022)

Learning to predict arbitrary quantum processes. - Hsin-Yuan Huang; Sitan Chen; John Preskill (CoRR, 2022)

Learning Narrow One-Hidden-Layer ReLU Networks. - Sitan Chen; Zehao Dou; Surbhi Goel; Adam R. Klivans; Raghu Meka (COLT, 2023)

When Does Adaptivity Help for Quantum State Learning? - Sitan Chen; Brice Huang; Jerry Li 001; Allen Liu; Mark Sellke (FOCS, 2023)

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. - Sitan Chen; Sinho Chewi; Jerry Li 001; Yuanzhi Li; Adil Salim; Anru Zhang (ICLR, 2023)

Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. - Sitan Chen; Giannis Daras; Alex Dimakis (ICML, 2023)

The probability flow ODE is provably fast. - Sitan Chen; Sinho Chewi; Holden Lee; Yuanzhi Li; Jianfeng Lu 001; Adil Salim (NeurIPS, 2023)

Learning Mixtures of Gaussians Using the DDPM Objective. - Kulin Shah; Sitan Chen; Adam R. Klivans (NeurIPS, 2023)

Learning Polynomial Transformations via Generalized Tensor Decompositions. - Sitan Chen; Jerry Li 001; Yuanzhi Li; Anru R. Zhang (STOC, 2023)

Futility and utility of a few ancillas for Pauli channel learning. - Sitan Chen; Weiyuan Gong (CoRR, 2023)

A faster and simpler algorithm for learning shallow networks. - Sitan Chen; Shyam Narayanan (COLT, 2024)

Optimal Tradeoffs for Estimating Pauli Observables. - Sitan Chen; Weiyuan Gong; Qi Ye (FOCS, 2024)

Critical windows: non-asymptotic theory for feature emergence in diffusion models. - Marvin Li; Sitan Chen (ICML, 2024)

What does guidance do? A fine-grained analysis in a simple setting. - Muthu Chidambaram; Khashayar Gatmiry; Sitan Chen; Holden Lee; Jianfeng Lu 001 (NeurIPS, 2024)

Unrolled denoising networks provably learn to perform optimal Bayesian inference. - Aayush Karan; Kulin Shah; Sitan Chen; Yonina C. Eldar (NeurIPS, 2024)

An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. - Sitan Chen; Jerry Li 001; Allen Liu (STOC, 2024)

Faster Diffusion-based Sampling with Randomized Midpoints: Sequential and Parallel. - Shivam Gupta 002; Linda Cai; Sitan Chen (CoRR, 2024)

Optimal high-precision shadow estimation. - Sitan Chen; Jerry Li 001; Allen Liu (CoRR, 2024)

Unrolled denoising networks provably learn optimal Bayesian inference. - Aayush Karan; Kulin Shah; Sitan Chen; Yonina C. Eldar (CoRR, 2024)

Gradient dynamics for low-rank fine-tuning beyond kernels. - Arif Kerem Dayi; Sitan Chen (CoRR, 2024)

Adaptivity can help exponentially for shadow tomography. - Sitan Chen; Weiyuan Gong; Zhihan Zhang (CoRR, 2024)

Learning general Gaussian mixtures with efficient score matching. - Sitan Chen; Vasilis Kontonis; Kulin Shah (COLT, 2025)

Predicting quantum channels over general product distributions. - Sitan Chen; Jaume de Dios Pont; Jun-Ting Hsieh; Hsin-Yuan Huang; Jane Lange; Jerry Li 001 (COLT, 2025)

Low-rank fine-tuning lies between lazy training and feature learning. - Arif Kerem Dayi; Sitan Chen (COLT, 2025)

Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel. - Shivam Gupta 002; Linda Cai; Sitan Chen (ICLR, 2025)

Stabilizer Bootstrapping: A Recipe for Efficient Agnostic Tomography and Magic Estimation. - Sitan Chen; Weiyuan Gong; Qi Ye; Zhihan Zhang (STOC, 2025)

Provably Learning a Multi-head Attention Layer. - Sitan Chen; Yuanzhi Li (STOC, 2025)

Blink of an eye: a simple theory for feature localization in generative models. - Marvin Li; Aayush Karan; Sitan Chen (CoRR, 2025)

Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions. - Jaeyeon Kim; Kulin Shah; Vasilis Kontonis; Sham M. Kakade; Sitan Chen (CoRR, 2025)

S4S: Solving for a Diffusion Model Solver. - Eric Frankel; Sitan Chen; Jerry Li 001; Pang Wei Koh; Lillian J. Ratliff; Sewoong Oh (CoRR, 2025)

Information-Computation Gaps in Quantum Learning via Low-Degree Likelihood. - Sitan Chen; Weiyuan Gong; Jonas Haferkamp; Yihui Quek (CoRR, 2025)

ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems. - Aayush Karan; Kulin Shah; Sitan Chen (CoRR, 2025)