Sinho Chewi

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 54

Top Topics

Fisher Information

Variational Inference

Top Venues

Publications

Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan Bill Li
Sampling from the Mean-Field Stationary Distribution. CoRR (2024)
Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li
Sampling from the Mean-Field Stationary Distribution. COLT (2024)
Jason M. Altschuler, Sinho Chewi
Shifted Composition II: Shift Harnack Inequalities and Curvature Upper Bounds. CoRR (2024)
Nima Anari, Sinho Chewi, Thuy-Duong Vuong
Fast parallel sampling under isoperimetry. COLT (2024)
Nima Anari, Sinho Chewi, Thuy-Duong Vuong
Fast parallel sampling under isoperimetry. CoRR (2024)
Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space. COLT (2024)
Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
Query Lower Bounds for Log-concave Sampling. J. ACM 71 (4) (2024)
Jason M. Altschuler, Sinho Chewi
Faster High-accuracy Log-concave Sampling via Algorithmic Warm Starts. J. ACM 71 (3) (2024)
Matthew Shunshi Zhang, Sinho Chewi, Mufan (Bill) Li, Krishna Balasubramanian, Murat A. Erdogdu
Improved Discretization Analysis for Underdamped Langevin Monte Carlo. COLT (2023)
Sinho Chewi, Patrik Gerber, Holden Lee, Chen Lu
Fisher information lower bounds for sampling. ALT (2023)
Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim
Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space. ICML (2023)
Jason M. Altschuler, Sinho Chewi
Faster high-accuracy log-concave sampling via algorithmic warm starts. FOCS (2023)
Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
Query lower bounds for log-concave sampling. CoRR (2023)
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
The probability flow ODE is provably fast. CoRR (2023)
Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim
Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. CoRR (2023)
Sinho Chewi, Sébastien Bubeck, Adil Salim
On the complexity of finding stationary points of smooth functions in one dimension. ALT (2023)
Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan
Query lower bounds for log-concave sampling. FOCS (2023)
Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim
The probability flow ODE is provably fast. NeurIPS (2023)
Jason M. Altschuler, Sinho Chewi
Faster high-accuracy log-concave sampling via algorithmic warm starts. CoRR (2023)
Jason M. Altschuler, Sinho Chewi
Shifted Composition I: Harnack and Reverse Transport Inequalities. CoRR (2023)
Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian
Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space. CoRR (2023)
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
Learning threshold neurons via edge of stability. NeurIPS (2023)
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru Zhang
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. ICLR (2023)
Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono
Improved analysis for a proximal algorithm for sampling. COLT (2022)
Sinho Chewi, Patrik Gerber, Holden Lee, Chen Lu
Fisher information lower bounds for sampling. CoRR (2022)
Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet
Variational inference via Wasserstein gradient flows. NeurIPS (2022)
Krishna Balasubramanian, Sinho Chewi, Murat A. Erdogdu, Adil Salim, Shunshi Zhang
Towards a Theory of Non-Log-Concave Sampling: First-Order Stationarity Guarantees for Langevin Monte Carlo. COLT (2022)
Sinho Chewi, Patrik Gerber, Philippe Rigollet, Paxton Turner
Gaussian discrepancy: A probabilistic relaxation of vector balancing. Discret. Appl. Math. 322 (2022)
Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet
Variational inference via Wasserstein gradient flows. CoRR (2022)
Sitan Chen, Sinho Chewi, Jerry Li, Yuanzhi Li, Adil Salim, Anru R. Zhang
Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. CoRR (2022)
Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li, Ruoqi Shen, Shunshi Zhang
Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. COLT (2022)
Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
Rejection sampling from shape-constrained distributions in sublinear time. AISTATS (2022)
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang
Learning threshold neurons via the "edge of stability". CoRR (2022)
Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
The query complexity of sampling from strongly log-concave distributions in one dimension. COLT (2022)
Sinho Chewi, Patrik Gerber, Philippe Rigollet, Paxton Turner
Gaussian discrepancy: a probabilistic relaxation of vector balancing. CoRR (2021)
Sinho Chewi
The entropic barrier is n-self-concordant. CoRR (2021)
Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin Stromme
Fast and Smooth Interpolation on Wasserstein Space. AISTATS (2021)
Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. COLT (2021)
Sinho Chewi, Patrik Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
The query complexity of sampling from strongly log-concave distributions in one dimension. CoRR (2021)
Sinho Chewi, Patrik Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet
Rejection sampling from shape-constrained distributions in sublinear time. CoRR (2021)
Jason M. Altschuler, Sinho Chewi, Patrik Gerber, Austin J. Stromme
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent. CoRR (2021)
Kwangjun Ahn, Sinho Chewi
Efficient constrained sampling via the mirror-Langevin algorithm. NeurIPS (2021)
Jason M. Altschuler, Sinho Chewi, Patrik Gerber, Austin J. Stromme
Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent. NeurIPS (2021)
Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin Stromme
Exponential ergodicity of mirror-Langevin diffusions. NeurIPS (2020)
Kwangjun Ahn, Sinho Chewi
Efficient constrained sampling via the mirror-Langevin algorithm. CoRR (2020)
Sinho Chewi, Tyler Maunu, Philippe Rigollet, Austin J. Stromme
Gradient descent algorithms for Bures-Wasserstein barycenters. COLT (2020)
Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet
Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. CoRR (2020)
Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. CoRR (2020)
Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet
SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. NeurIPS (2020)
Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin Stromme
Exponential ergodicity of mirror-Langevin diffusions. CoRR (2020)
Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, Kannan Ramchandran
Matching Observations to Distributions: Efficient Estimation via Sparsified Hungarian Algorithm. Allerton (2019)
Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, Kannan Ramchandran
Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach. CoRR (2018)
Sinho Chewi, Venkat Anantharam
A Combinatorial Proof of a Formula of Biane and Chapuy. Electron. J. Comb. 25 (1) (2018)
Sinho Chewi, Venkat Anantharam
A combinatorial proof of a formula of Biane and Chapuy. CoRR (2017)