Chewi, Sinho

26 publications

ICLRW 2025 DDPM Score Matching Is Asymptotically Efficient Sinho Chewi, Alkis Kalavasis, Anay Mehrotra, Omar Montasser

TMLR 2025 Gaussian Mixture Layers for Neural Networks Sinho Chewi, Philippe Rigollet, Yuling Yan

COLT 2024 Algorithms for Mean-Field Variational Inference via Polyhedral Optimization in the Wasserstein Space Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian

COLT 2024 Fast Parallel Sampling Under Isoperimetry Nima Anari, Sinho Chewi, Thuy-Duong Vuong

COLT 2024 Sampling from the Mean-Field Stationary Distribution Yunbum Kook, Matthew S. Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan Li

ALT 2023 Fisher Information Lower Bounds for Sampling Sinho Chewi, Patrik Gerber, Holden Lee, Chen Lu

ICML 2023 Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim

COLT 2023 Improved Discretization Analysis for Underdamped Langevin Monte Carlo Shunshi Zhang, Sinho Chewi, Mufan Li, Krishna Balasubramanian, Murat A. Erdogdu

NeurIPS 2023 Learning Threshold Neurons via Edge of Stability Kwangjun Ahn, Sebastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang

ALT 2023 On the Complexity of Finding Stationary Points of Smooth Functions in One Dimension Sinho Chewi, Sébastien Bubeck, Adil Salim

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

AISTATS 2022 Rejection Sampling from Shape-Constrained Distributions in Sublinear Time Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet

COLT 2022 Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev Sinho Chewi, Murat A Erdogdu, Mufan Li, Ruoqi Shen, Shunshi Zhang

COLT 2022 Improved Analysis for a Proximal Algorithm for Sampling Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono

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 The Query Complexity of Sampling from Strongly Log-Concave Distributions in One Dimension Sinho Chewi, Patrik R Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet

COLT 2022 Towards a Theory of Non-Log-Concave Sampling:First-Order Stationarity Guarantees for Langevin Monte Carlo Krishna Balasubramanian, Sinho Chewi, Murat A Erdogdu, Adil Salim, Shunshi Zhang

NeurIPS 2022 Variational Inference via Wasserstein Gradient Flows Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet

AISTATS 2021 Fast and Smooth Interpolation on Wasserstein Space Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin Stromme

NeurIPS 2021 Averaging on the Bures-Wasserstein Manifold: Dimension-Free Convergence of Gradient Descent Jason Altschuler, Sinho Chewi, Patrik R Gerber, Austin Stromme

NeurIPS 2021 Efficient Constrained Sampling via the Mirror-Langevin Algorithm Kwangjun Ahn, Sinho Chewi

COLT 2021 Optimal Dimension Dependence of the Metropolis-Adjusted Langevin Algorithm Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet

NeurIPS 2020 Exponential Ergodicity of Mirror-Langevin Diffusions Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin Stromme

COLT 2020 Gradient Descent Algorithms for Bures-Wasserstein Barycenters Sinho Chewi, Tyler Maunu, Philippe Rigollet, Austin J. Stromme

NeurIPS 2020 SVGD as a Kernelized Wasserstein Gradient Flow of the Chi-Squared Divergence Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet