| 2026 | COLT | Optimal Inference Schedules for Masked Diffusion Models. | Sitan Chen, Kevin Cong, Jerry Li |
| 2026 | COLT | Information-computation gaps in quantum learning via low-degree likelihood. | Sitan Chen, Weiyuan Gong, Jonas Haferkamp, Yihui Quek |
| 2026 | STOC | Computation-Utility-Privacy Tradeoffs in Bayesian Estimation. | Sitan Chen, Jingqiu Ding, Mahbod Majid, Walter McKelvie |
| 2025 | COLT | Learning general Gaussian mixtures with efficient score matching. | Sitan Chen, Vasilis Kontonis, Kulin Shah |
| 2025 | COLT | Predicting quantum channels over general product distributions. | Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li |
| 2025 | COLT | Low-rank fine-tuning lies between lazy training and feature learning. | Arif Kerem Dayi, Sitan Chen |
| 2025 | ICLR | Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel. | Shivam Gupta, Linda Cai, Sitan Chen |
| 2025 | ICML | S4S: Solving for a Fast Diffusion Model Solver. | Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh |
| 2025 | ICML | 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 |
| 2025 | ICML | Blink of an eye: a simple theory for feature localization in generative models. | Marvin Li, Aayush Karan, Sitan Chen |
| 2025 | STOC | Stabilizer Bootstrapping: A Recipe for Efficient Agnostic Tomography and Magic Estimation. | Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang |
| 2025 | STOC | Provably Learning a Multi-head Attention Layer. | Sitan Chen, Yuanzhi Li |
| 2024 | COLT | A faster and simpler algorithm for learning shallow networks. | Sitan Chen, Shyam Narayanan |
| 2024 | FOCS | Optimal Tradeoffs for Estimating Pauli Observables. | Sitan Chen, Weiyuan Gong, Qi Ye |
| 2024 | ICML | Critical windows: non-asymptotic theory for feature emergence in diffusion models. | Marvin Li, Sitan Chen |
| 2024 | STOC | An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. | Sitan Chen, Jerry Li, Allen Liu |
| 2023 | COLT | Learning Narrow One-Hidden-Layer ReLU Networks. | Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka |
| 2023 | FOCS | When Does Adaptivity Help for Quantum State Learning? | Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke |
| 2023 | ICLR | Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. | Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang |
| 2023 | ICML | Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. | Sitan Chen, Giannis Daras, Alex Dimakis |
| 2023 | STOC | Learning Polynomial Transformations via Generalized Tensor Decompositions. | Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang |
| 2022 | COLT | Toward Instance-Optimal State Certification With Incoherent Measurements. | Sitan Chen, Jerry Li, Ryan O'Donnell |
| 2022 | FOCS | Tight Bounds for Quantum State Certification with Incoherent Measurements. | Sitan Chen, Jerry Li, Brice Huang, Allen Liu |
| 2022 | ICLR | Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. | Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka |
| 2022 | STOC | Kalman filtering with adversarial corruptions. | Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2021 | FOCS | Exponential Separations Between Learning With and Without Quantum Memory. | Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li |
| 2021 | FOCS | Learning Deep ReLU Networks Is Fixed-Parameter Tractable. | Sitan Chen, Adam R. Klivans, Raghu Meka |
| 2021 | FOCS | Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. | Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau |
| 2021 | ICLR | On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. | Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo |
| 2021 | STOC | Algorithmic foundations for the diffraction limit. | Sitan Chen, Ankur Moitra |
| 2020 | COLT | Learning Polynomials in Few Relevant Dimensions. | Sitan Chen, Raghu Meka |
| 2020 | FOCS | Entanglement is Necessary for Optimal Quantum Property Testing. | Sbastien Bubeck, Sitan Chen, Jerry Li |
| 2020 | STOC | Efficiently learning structured distributions from untrusted batches. | Sitan Chen, Jerry Li, Ankur Moitra |
| 2020 | STOC | Learning mixtures of linear regressions in subexponential time via Fourier moments. | Sitan Chen, Jerry Li, Zhao Song |
| 2019 | SODA | Improved Bounds for Randomly Sampling Colorings via Linear Programming. | Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle |
| 2019 | STOC | Beyond the low-degree algorithm: mixtures of subcubes and their applications. | Sitan Chen, Ankur Moitra |
| 2016 | STOC | Basis collapse for holographic algorithms over all domain sizes. | Sitan Chen |