Sitan Chen - RankMe

113 papers A* 43A 1Journal 68

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Computation-Utility-Privacy Tradeoffs in Bayesian Estimation. CoRR Sitan Chen, Jingqiu Ding, Mahbod Majid, Walter McKelvie
2026	J	`jnl`	High-accuracy and dimension-free sampling with diffusions. CoRR Khashayar Gatmiry, Sitan Chen, Adil Salim
2026	J	`jnl`	Stop Training for the Worst: Progressive Unmasking Accelerates Masked Diffusion Training. CoRR Jaeyeon Kim, Jonathan Geuter, David Alvarez-Melis, Sham M. Kakade, Sitan Chen
2025	J	`jnl`	A Provably Efficient Method for Tensor Ring Decomposition and Its Applications. CoRR Han Chen, Sitan Chen, Anru R. Zhang
2025	J	`jnl`	Any-Order Flexible Length Masked Diffusion. CoRR Jaeyeon Kim, Cheuk Kit Lee, Carles Domingo-Enrich, Yilun Du, Sham M. Kakade, Timothy Ngotiaoco, Sitan Chen, Michael S. Albergo
2025	A*	`conf`	Blink of an eye: a simple theory for feature localization in generative models. ICML Marvin Li, Aayush Karan, Sitan Chen
2025	J	`jnl`	Blink of an eye: a simple theory for feature localization in generative models. CoRR Marvin Li, Aayush Karan, Sitan Chen
2025	J	`jnl`	Catching the Blackdog Easily: A Convenient Depression Diagnosis Method Based on Audio-Visual Deep Learning. IEEE Trans. Affect. Comput. Linfeng Liu, Sitan Chen, Ke-Jia Chen, Jiajun Xu, Xiacan Chen
2025	A*	`conf`	Faster Diffusion Sampling with Randomized Midpoints: Sequential and Parallel. ICLR Shivam Gupta, Linda Cai, Sitan Chen
2025	J	`jnl`	Fine-Tuning Masked Diffusion for Provable Self-Correction. CoRR Jaeyeon Kim, Seunggeun Kim, Taekyun Lee, David Z. Pan, Hyeji Kim, Sham M. Kakade, Sitan Chen
2025	J	`jnl`	Information-Computation Gaps in Quantum Learning via Low-Degree Likelihood. CoRR Sitan Chen, Weiyuan Gong, Jonas Haferkamp, Yihui Quek
2025	A*	`conf`	Learning general Gaussian mixtures with efficient score matching. COLT Sitan Chen, Vasilis Kontonis, Kulin Shah
2025	A*	`conf`	Low-rank fine-tuning lies between lazy training and feature learning. COLT Arif Kerem Dayi, Sitan Chen
2025	J	`jnl`	Optimal Inference Schedules for Masked Diffusion Models. CoRR Sitan Chen, Kevin Cong, Jerry Li
2025	A*	`conf`	Predicting quantum channels over general product distributions. COLT Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li
2025	A*	`conf`	Provably Learning a Multi-head Attention Layer. STOC Sitan Chen, Yuanzhi Li
2025	J	`jnl`	Quantum Probe Tomography. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang
2025	J	`jnl`	ReGuidance: A Simple Diffusion Wrapper for Boosting Sample Quality on Hard Inverse Problems. CoRR Aayush Karan, Kulin Shah, Sitan Chen
2025	J	`jnl`	S4S: Solving for a Diffusion Model Solver. CoRR Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
2025	A*	`conf`	S4S: Solving for a Fast Diffusion Model Solver. ICML Eric Frankel, Sitan Chen, Jerry Li, Pang Wei Koh, Lillian J. Ratliff, Sewoong Oh
2025	J	`jnl`	Selective Underfitting in Diffusion Models. CoRR Kiwhan Song, Jaeyeon Kim, Sitan Chen, Yilun Du, Sham M. Kakade, Vincent Sitzmann
2025	A*	`conf`	Stabilizer Bootstrapping: A Recipe for Efficient Agnostic Tomography and Magic Estimation. STOC Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang
2025	J	`jnl`	Sublinear iterations can suffice even for DDPMs. CoRR Matthew S. Zhang, Stephen Huan, Jerry Huang, Nicholas M. Boffi, Sitan Chen, Sinho Chewi
2025	A*	`conf`	Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions. ICML Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, Sitan Chen
2025	J	`jnl`	Train for the Worst, Plan for the Best: Understanding Token Ordering in Masked Diffusions. CoRR Jaeyeon Kim, Kulin Shah, Vasilis Kontonis, Sham M. Kakade, Sitan Chen
2025	J	`jnl`	Unlocking Mobile Phones by Rolling Wrists: A Novel Motion-Based Biometric Recognition Method. IEEE Trans. Inf. Forensics Secur. Yadi He, Sitan Chen, Linfeng Liu
2024	A*	`conf`	A faster and simpler algorithm for learning shallow networks. COLT Sitan Chen, Shyam Narayanan
2024	J	`jnl`	Adaptivity can help exponentially for shadow tomography. CoRR Sitan Chen, Weiyuan Gong, Zhihan Zhang
2024	A*	`conf`	An Optimal Tradeoff between Entanglement and Copy Complexity for State Tomography. STOC Sitan Chen, Jerry Li, Allen Liu
2024	J	`jnl`	An optimal tradeoff between entanglement and copy complexity for state tomography. CoRR Sitan Chen, Jerry Li, Allen Liu
2024	A*	`conf`	Critical windows: non-asymptotic theory for feature emergence in diffusion models. ICML Marvin Li, Sitan Chen
2024	J	`jnl`	Critical windows: non-asymptotic theory for feature emergence in diffusion models. CoRR Marvin Li, Sitan Chen
2024	J	`jnl`	Faster Diffusion-based Sampling with Randomized Midpoints: Sequential and Parallel. CoRR Shivam Gupta, Linda Cai, Sitan Chen
2024	J	`jnl`	Gradient dynamics for low-rank fine-tuning beyond kernels. CoRR Arif Kerem Dayi, Sitan Chen
2024	J	`jnl`	Learning general Gaussian mixtures with efficient score matching. CoRR Sitan Chen, Vasilis Kontonis, Kulin Shah
2024	A*	`conf`	Optimal Tradeoffs for Estimating Pauli Observables. FOCS Sitan Chen, Weiyuan Gong, Qi Ye
2024	J	`jnl`	Optimal high-precision shadow estimation. CoRR Sitan Chen, Jerry Li, Allen Liu
2024	J	`jnl`	Optimal tradeoffs for estimating Pauli observables. CoRR Sitan Chen, Weiyuan Gong, Qi Ye
2024	J	`jnl`	Predicting quantum channels over general product distributions. CoRR Sitan Chen, Jaume de Dios Pont, Jun-Ting Hsieh, Hsin-Yuan Huang, Jane Lange, Jerry Li
2024	J	`jnl`	Provably learning a multi-head attention layer. CoRR Sitan Chen, Yuanzhi Li
2024	J	`jnl`	Stabilizer bootstrapping: A recipe for efficient agnostic tomography and magic estimation. CoRR Sitan Chen, Weiyuan Gong, Qi Ye, Zhihan Zhang
2024	J	`jnl`	Unrolled denoising networks provably learn optimal Bayesian inference. CoRR Aayush Karan, Kulin Shah, Sitan Chen, Yonina C. Eldar
2024	A*	`conf`	Unrolled denoising networks provably learn to perform optimal Bayesian inference. NeurIPS Aayush Karan, Kulin Shah, Sitan Chen, Yonina C. Eldar
2024	A*	`conf`	What does guidance do? A fine-grained analysis in a simple setting. NeurIPS Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu
2024	J	`jnl`	What does guidance do? A fine-grained analysis in a simple setting. CoRR Muthu Chidambaram, Khashayar Gatmiry, Sitan Chen, Holden Lee, Jianfeng Lu
2023	J	`jnl`	A faster and simpler algorithm for learning shallow networks. CoRR Sitan Chen, Shyam Narayanan
2023	J	`jnl`	Futility and utility of a few ancillas for Pauli channel learning. CoRR Sitan Chen, Weiyuan Gong
2023	A*	`conf`	Learning Mixtures of Gaussians Using the DDPM Objective. NeurIPS Kulin Shah, Sitan Chen, Adam R. Klivans
2023	J	`jnl`	Learning Mixtures of Gaussians Using the DDPM Objective. CoRR Kulin Shah, Sitan Chen, Adam R. Klivans
2023	A*	`conf`	Learning Narrow One-Hidden-Layer ReLU Networks. COLT Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
2023	J	`jnl`	Learning Narrow One-Hidden-Layer ReLU Networks. CoRR Sitan Chen, Zehao Dou, Surbhi Goel, Adam R. Klivans, Raghu Meka
2023	A*	`conf`	Learning Polynomial Transformations via Generalized Tensor Decompositions. STOC Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
2023	J	`jnl`	Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-Type Samplers. CoRR Sitan Chen, Giannis Daras, Alexandros G. Dimakis
2023	A*	`conf`	Restoration-Degradation Beyond Linear Diffusions: A Non-Asymptotic Analysis For DDIM-type Samplers. ICML Sitan Chen, Giannis Daras, Alex Dimakis
2023	A*	`conf`	Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. ICLR Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang
2023	A*	`conf`	The probability flow ODE is provably fast. NeurIPS Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
2023	J	`jnl`	The probability flow ODE is provably fast. CoRR Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
2023	A*	`conf`	When Does Adaptivity Help for Quantum State Learning? FOCS Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
2022	A*	`conf`	Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. NeurIPS Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
2022	J	`jnl`	Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks. CoRR Sitan Chen, Aravind Gollakota, Adam R. Klivans, Raghu Meka
2022	A*	`conf`	Kalman filtering with adversarial corruptions. STOC Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2022	A*	`conf`	Learning (Very) Simple Generative Models Is Hard. NeurIPS Sitan Chen, Jerry Li, Yuanzhi Li
2022	J	`jnl`	Learning (Very) Simple Generative Models Is Hard. CoRR Sitan Chen, Jerry Li, Yuanzhi Li
2022	J	`jnl`	Learning Polynomial Transformations. CoRR Sitan Chen, Jerry Li, Yuanzhi Li, Anru R. Zhang
2022	J	`jnl`	Learning to predict arbitrary quantum processes. CoRR Hsin-Yuan Huang, Sitan Chen, John Preskill
2022	A*	`conf`	Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. ICLR Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
2022	J	`jnl`	Minimax Optimality (Probably) Doesn't Imply Distribution Learning for GANs. CoRR Sitan Chen, Jerry Li, Yuanzhi Li, Raghu Meka
2022	J	`jnl`	Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. CoRR Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
2022	A	`conf`	Symmetric Sparse Boolean Matrix Factorization and Applications. ITCS Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
2022	J	`jnl`	The Complexity of NISQ. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2022	A*	`conf`	Tight Bounds for Quantum State Certification with Incoherent Measurements. FOCS Sitan Chen, Jerry Li, Brice Huang, Allen Liu
2022	J	`jnl`	Tight Bounds for Quantum State Certification with Incoherent Measurements. CoRR Sitan Chen, Brice Huang, Jerry Li, Allen Liu
2022	J	`jnl`	Tight Bounds for State Tomography with Incoherent Measurements. CoRR Sitan Chen, Brice Huang, Jerry Li, Allen Liu, Mark Sellke
2022	A*	`conf`	Toward Instance-Optimal State Certification With Incoherent Measurements. COLT Sitan Chen, Jerry Li, Ryan O'Donnell
2021	J	`jnl`	A Hierarchy for Replica Quantum Advantage. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2021	A*	`conf`	Algorithmic foundations for the diffraction limit. STOC Sitan Chen, Ankur Moitra
2021	J	`jnl`	Efficiently Learning Any One Hidden Layer ReLU Network From Queries. CoRR Sitan Chen, Adam R. Klivans, Raghu Meka
2021	A*	`conf`	Efficiently Learning One Hidden Layer ReLU Networks From Queries. NeurIPS Sitan Chen, Adam R. Klivans, Raghu Meka
2021	A*	`conf`	Exponential Separations Between Learning With and Without Quantum Memory. FOCS Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2021	J	`jnl`	Exponential separations between learning with and without quantum memory. CoRR Sitan Chen, Jordan Cotler, Hsin-Yuan Huang, Jerry Li
2021	J	`jnl`	Kalman Filtering with Adversarial Corruptions. CoRR Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2021	A*	`conf`	Learning Deep ReLU Networks Is Fixed-Parameter Tractable. FOCS Sitan Chen, Adam R. Klivans, Raghu Meka
2021	A*	`conf`	On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. ICLR Sitan Chen, Xiaoxiao Li, Zhao Song, Danyang Zhuo
2021	A*	`conf`	Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. FOCS Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2021	J	`jnl`	Quantum advantage in learning from experiments. CoRR Hsin-Yuan Huang, Michael Broughton, Jordan Cotler, Sitan Chen, Jerry Li, Masoud Mohseni, Hartmut Neven, Ryan Babbush, Richard Kueng, John Preskill, Jarrod R. McClean
2021	—	`—`	Rethinking Algorithm Design for Modern Challenges in Data Science. Sitan Chen
2021	J	`jnl`	Symmetric Boolean Factor Analysis with Applications to InstaHide. CoRR Sitan Chen, Zhao Song, Runzhou Tao, Ruizhe Zhang
2021	J	`jnl`	Toward Instance-Optimal State Certification With Incoherent Measurements. CoRR Sitan Chen, Jerry Li, Ryan O'Donnell
2020	J	`jnl`	Algorithmic Foundations for the Diffraction Limit. CoRR Sitan Chen, Ankur Moitra
2020	J	`jnl`	Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Connections to Evolvability. CoRR Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2020	A*	`conf`	Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability. NeurIPS Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2020	A*	`conf`	Efficiently learning structured distributions from untrusted batches. STOC Sitan Chen, Jerry Li, Ankur Moitra
2020	A*	`conf`	Entanglement is Necessary for Optimal Quantum Property Testing. FOCS Sébastien Bubeck, Sitan Chen, Jerry Li
2020	J	`jnl`	Entanglement is Necessary for Optimal Quantum Property Testing. CoRR Sébastien Bubeck, Sitan Chen, Jerry Li
2020	J	`jnl`	Learning Deep ReLU Networks Is Fixed-Parameter Tractable. CoRR Sitan Chen, Adam R. Klivans, Raghu Meka
2020	A*	`conf`	Learning Polynomials in Few Relevant Dimensions. COLT Sitan Chen, Raghu Meka
2020	J	`jnl`	Learning Polynomials of Few Relevant Dimensions. CoRR Sitan Chen, Raghu Meka
2020	A*	`conf`	Learning Structured Distributions From Untrusted Batches: Faster and Simpler. NeurIPS Sitan Chen, Jerry Li, Ankur Moitra
2020	J	`jnl`	Learning Structured Distributions From Untrusted Batches: Faster and Simpler. CoRR Sitan Chen, Jerry Li, Ankur Moitra
2020	A*	`conf`	Learning mixtures of linear regressions in subexponential time via Fourier moments. STOC Sitan Chen, Jerry Li, Zhao Song
2020	J	`jnl`	On InstaHide, Phase Retrieval, and Sparse Matrix Factorization. CoRR Sitan Chen, Zhao Song, Danyang Zhuo
2020	J	`jnl`	Online and Distribution-Free Robustness: Regression and Contextual Bandits with Huber Contamination. CoRR Sitan Chen, Frederic Koehler, Ankur Moitra, Morris Yau
2019	A*	`conf`	Beyond the low-degree algorithm: mixtures of subcubes and their applications. STOC Sitan Chen, Ankur Moitra
2019	J	`jnl`	Efficiently Learning Structured Distributions from Untrusted Batches. CoRR Sitan Chen, Jerry Li, Ankur Moitra
2019	A*	`conf`	Improved Bounds for Randomly Sampling Colorings via Linear Programming. SODA Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
2019	J	`jnl`	Learning Mixtures of Linear Regressions in Subexponential Time via Fourier Moments. CoRR Sitan Chen, Jerry Li, Zhao Song
2018	J	`jnl`	Improved Bounds for Randomly Sampling Colorings via Linear Programming. CoRR Sitan Chen, Michelle Delcourt, Ankur Moitra, Guillem Perarnau, Luke Postle
2018	J	`jnl`	Learning Mixtures of Product Distributions via Higher Multilinear Moments. CoRR Sitan Chen, Ankur Moitra
2018	J	`jnl`	Linear Programming Bounds for Randomly Sampling Colorings. CoRR Sitan Chen, Ankur Moitra
2016	A*	`conf`	Basis collapse for holographic algorithms over all domain sizes. STOC Sitan Chen
2015	J	`jnl`	Basis Collapse for Holographic Algorithms Over All Domain Sizes. CoRR Sitan Chen
2015	J	`jnl`	Pseudorandomness for Read-Once, Constant-Depth Circuits. CoRR Sitan Chen, Thomas Steinke, Salil P. Vadhan
2014	J	`jnl`	Cellular Automata to More Efficiently Compute the Collatz Map. Int. J. Unconv. Comput. Sitan Chen