Chen, Sitan

28 publications

ICML 2025 Blink of an Eye: A Simple Theory for Feature Localization in Generative Models Marvin Li, Aayush Karan, Sitan Chen

ICLRW 2025 Blink of an Eye: A Simple Theory for Feature Localization in Generative Models Marvin Li, Aayush Karan, Sitan Chen

ICLR 2025 Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel Shivam Gupta, Linda Cai, Sitan Chen

COLT 2025 Learning General Gaussian Mixtures with Efficient Score Matching Sitan Chen, Vasilis Kontonis, Kulin Shah

COLT 2025 Low-Rank Fine-Tuning Lies Between Lazy Training and Feature Learning Arif Kerem Dayi, Sitan Chen

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

ICML 2025 S4S: Solving for a Fast Diffusion Model Solver Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh

ICML 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

ICLRW 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

COLT 2024 A Faster and Simpler Algorithm for Learning Shallow Networks Sitan Chen, Shyam Narayanan

ICML 2024 Critical Windows: Non-Asymptotic Theory for Feature Emergence in Diffusion Models Marvin Li, Sitan Chen

NeurIPS 2024 Unrolled Denoising Networks Provably Learn to Perform Optimal Bayesian Inference Aayush Karan, Kulin Shah, Sitan Chen, Yonina C. Eldar

NeurIPS 2024 What Does Guidance Do? a Fine-Grained Analysis in a Simple Setting Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu

NeurIPS 2023 Learning Mixtures of Gaussians Using the DDPM Objective Kulin Shah, Sitan Chen, Adam Klivans

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

ICML 2023 Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis for DDIM-Type Samplers Sitan Chen, Giannis Daras, Alex Dimakis

ICLR 2023 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

NeurIPS 2023 The Probability Flow ODE Is Provably Fast Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim

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

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

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

NeurIPSW 2022 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

COLT 2022 Toward Instance-Optimal State Certification with Incoherent Measurements Sitan Chen, Jerry Li, Ryan O’Donnell

NeurIPS 2021 Efficiently Learning One Hidden Layer ReLU Networks from Queries Sitan Chen, Adam Klivans, Raghu Meka

ICLR 2021 On InstaHide, Phase Retrieval, and Sparse Matrix Factorization Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo

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

COLT 2020 Learning Polynomials in Few Relevant Dimensions Sitan Chen, Raghu Meka

NeurIPS 2020 Learning Structured Distributions from Untrusted Batches: Faster and Simpler Sitan Chen, Jerry Li, Ankur Moitra