A Combinatorial Proof of a Formula of Biane and Chapuy. - Sinho Chewi; Venkat Anantharam (Electron. J. Comb., 2018)

Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach. - Sinho Chewi; Forest Yang; Avishek Ghosh; Abhay Parekh; Kannan Ramchandran (CoRR, 2018)

Matching Observations to Distributions: Efficient Estimation via Sparsified Hungarian Algorithm. - Sinho Chewi; Forest Yang; Avishek Ghosh; Abhay Parekh; Kannan Ramchandran (Allerton, 2019)

Gradient descent algorithms for Bures-Wasserstein barycenters. - Sinho Chewi; Tyler Maunu; Philippe Rigollet; Austin J. Stromme (COLT, 2020)

SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence. - Sinho Chewi; Thibaut Le Gouic; Chen Lu 002; Tyler Maunu; Philippe Rigollet (NeurIPS, 2020)

Exponential ergodicity of mirror-Langevin diffusions. - Sinho Chewi; Thibaut Le Gouic; Chen Lu 002; Tyler Maunu; Philippe Rigollet; Austin J. Stromme (NeurIPS, 2020)

Fast and Smooth Interpolation on Wasserstein Space. - Sinho Chewi; Julien Clancy; Thibaut Le Gouic; Philippe Rigollet; George Stepaniants; Austin J. Stromme (AISTATS, 2021)

Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm. - Sinho Chewi; Chen Lu 002; Kwangjun Ahn; Xiang Cheng; Thibaut Le Gouic; Philippe Rigollet (COLT, 2021)

Efficient constrained sampling via the mirror-Langevin algorithm. - Kwangjun Ahn; Sinho Chewi (NeurIPS, 2021)

Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent. - Jason M. Altschuler; Sinho Chewi; Patrik Gerber; Austin J. Stromme (NeurIPS, 2021)

The entropic barrier is n-self-concordant. - Sinho Chewi (CoRR, 2021)

Rejection sampling from shape-constrained distributions in sublinear time. - Sinho Chewi; Patrik R. Gerber; Chen Lu 002; Thibaut Le Gouic; Philippe Rigollet (AISTATS, 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 001 (COLT, 2022)

Improved analysis for a proximal algorithm for sampling. - Yongxin Chen; Sinho Chewi; Adil Salim; Andre Wibisono (COLT, 2022)

Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev. - Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li; Ruoqi Shen; Shunshi Zhang 001 (COLT, 2022)

The query complexity of sampling from strongly log-concave distributions in one dimension. - Sinho Chewi; Patrik R. Gerber; Chen Lu 002; Thibaut Le Gouic; Philippe Rigollet (COLT, 2022)

Variational inference via Wasserstein gradient flows. - Marc Lambert; Sinho Chewi; Francis R. Bach; Silvère Bonnabel; Philippe Rigollet (NeurIPS, 2022)

Gaussian discrepancy: A probabilistic relaxation of vector balancing. - Sinho Chewi; Patrik Gerber; Philippe Rigollet; Paxton Turner (Discret. Appl. Math., 2022)

On the complexity of finding stationary points of smooth functions in one dimension. - Sinho Chewi; Sébastien Bubeck; Adil Salim (ALT, 2023)

Fisher information lower bounds for sampling. - Sinho Chewi; Patrik Gerber; Holden Lee; Chen Lu 002 (ALT, 2023)

Improved Discretization Analysis for Underdamped Langevin Monte Carlo. - Matthew Shunshi Zhang; Sinho Chewi; Mufan (Bill) Li; Krishna Balasubramanian; Murat A. Erdogdu (COLT, 2023)

Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions. - Sitan Chen; Sinho Chewi; Jerry Li 001; Yuanzhi Li; Adil Salim; Anru Zhang (ICLR, 2023)

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

Learning threshold neurons via edge of stability. - Kwangjun Ahn; Sébastien Bubeck; Sinho Chewi; Yin Tat Lee; Felipe Suarez; Yi Zhang (NeurIPS, 2023)

The probability flow ODE is provably fast. - Sitan Chen; Sinho Chewi; Holden Lee; Yuanzhi Li; Jianfeng Lu 001; Adil Salim (NeurIPS, 2023)

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space. - Michael Diao; Krishnakumar Balasubramanian 002; Sinho Chewi; Adil Salim (CoRR, 2023)

Fast parallel sampling under isoperimetry. - Nima Anari; Sinho Chewi; Thuy-Duong Vuong (COLT, 2024)

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space. - Yiheng Jiang; Sinho Chewi; Aram-Alexandre Pooladian (COLT, 2024)

Sampling from the Mean-Field Stationary Distribution. - Yunbum Kook; Matthew Shunshi Zhang; Sinho Chewi; Murat A. Erdogdu; Mufan (Bill) Li (COLT, 2024)

Shifted Composition II: Shift Harnack Inequalities and Curvature Upper Bounds. - Jason M. Altschuler; Sinho Chewi (CoRR, 2024)

Sampling from the Mean-Field Stationary Distribution. - Yunbum Kook; Matthew Shunshi Zhang; Sinho Chewi; Murat A. Erdogdu; Mufan Bill Li (CoRR, 2024)

Shifted Composition III: Local Error Framework for KL Divergence. - Jason M. Altschuler; Sinho Chewi (CoRR, 2024)

Faster High-accuracy Log-concave Sampling via Algorithmic Warm Starts. - Jason M. Altschuler; Sinho Chewi (J. ACM, 2024)

Query Lower Bounds for Log-concave Sampling. - Sinho Chewi; Jaume de Dios Pont; Jerry Li 001; Chen Lu 002; Shyam Narayanan (J. ACM, 2024)

Shifted Composition I: Harnack and Reverse Transport Inequalities. - Jason M. Altschuler; Sinho Chewi (IEEE Trans. Inf. Theory, 2025)

DDPM Score Matching and Distribution Learning. - Sinho Chewi; Alkis Kalavasis; Anay Mehrotra; Omar Montasser (CoRR, 2025)

Shifted Composition IV: Underdamped Langevin and Numerical Discretizations with Partial Acceleration. - Jason M. Altschuler; Sinho Chewi; Matthew S. Zhang (CoRR, 2025)