Sinho Chewi

According to our database, Sinho Chewi authored at least 33 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Sampling from the Mean-Field Stationary Distribution.

Yunbum Kook, Matthew Shunshi Zhang, Sinho Chewi, Murat A. Erdogdu, Mufan Bill Li

CoRR, 2024

Fast parallel sampling under isoperimetry.

Nima Anari, Sinho Chewi, Thuy-Duong Vuong

CoRR, 2024

Shifted Composition II: Shift Harnack Inequalities and Curvature Upper Bounds.

Jason M. Altschuler, Sinho Chewi

CoRR, 2024

2023

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space.

Yiheng Jiang, Sinho Chewi, Aram-Alexandre Pooladian

CoRR, 2023

Shifted Composition I: Harnack and Reverse Transport Inequalities.

Jason M. Altschuler, Sinho Chewi

CoRR, 2023

Forward-backward Gaussian variational inference via JKO in the Bures-Wasserstein Space.

Michael Diao, Krishnakumar Balasubramanian, Sinho Chewi, Adil Salim

CoRR, 2023

The probability flow ODE is provably fast.

Sitan Chen, Sinho Chewi, Holden Lee, Yuanzhi Li, Jianfeng Lu, Adil Salim

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Learning threshold neurons via edge of stability.

Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Forward-Backward Gaussian Variational Inference via JKO in the Bures-Wasserstein Space.

Michael Ziyang Diao, Krishna Balasubramanian, Sinho Chewi, Adil Salim

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Query lower bounds for log-concave sampling.

Sinho Chewi, Jaume de Dios Pont, Jerry Li, Chen Lu, Shyam Narayanan

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

Faster high-accuracy log-concave sampling via algorithmic warm starts.

Jason M. Altschuler, Sinho Chewi

Proceedings of the 64th IEEE Annual Symposium on Foundations of Computer Science, 2023

Improved Discretization Analysis for Underdamped Langevin Monte Carlo.

Matthew Shunshi Zhang, Sinho Chewi, Mufan (Bill) Li, Krishna Balasubramanian, Murat A. Erdogdu

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Fisher information lower bounds for sampling.

Sinho Chewi, Patrik Gerber, Holden Lee, Chen Lu

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

On the complexity of finding stationary points of smooth functions in one dimension.

Sinho Chewi, Sébastien Bubeck, Adil Salim

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

Gaussian discrepancy: A probabilistic relaxation of vector balancing.

Sinho Chewi, Patrik Gerber, Philippe Rigollet, Paxton Turner

Discret. Appl. Math., 2022

Variational inference via Wasserstein gradient flows.

Marc Lambert, Sinho Chewi, Francis R. Bach, Silvère Bonnabel, Philippe Rigollet

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 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

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev.

Sinho Chewi, Murat A. Erdogdu, Mufan (Bill) Li, Ruoqi Shen, Shunshi Zhang

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Improved analysis for a proximal algorithm for sampling.

Yongxin Chen, Sinho Chewi, Adil Salim, Andre Wibisono

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 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

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

Rejection sampling from shape-constrained distributions in sublinear time.

Sinho Chewi, Patrik R. Gerber, Chen Lu, Thibaut Le Gouic, Philippe Rigollet

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

The entropic barrier is n-self-concordant.

Sinho Chewi

CoRR, 2021

Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent.

Jason M. Altschuler, Sinho Chewi, Patrik Gerber, Austin J. Stromme

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Efficient constrained sampling via the mirror-Langevin algorithm.

Kwangjun Ahn, Sinho Chewi

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal dimension dependence of the Metropolis-Adjusted Langevin Algorithm.

Sinho Chewi, Chen Lu, Kwangjun Ahn, Xiang Cheng, Thibaut Le Gouic, Philippe Rigollet

Proceedings of the Conference on Learning Theory, 2021

Fast and Smooth Interpolation on Wasserstein Space.

Sinho Chewi, Julien Clancy, Thibaut Le Gouic, Philippe Rigollet, George Stepaniants, Austin J. Stromme

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Exponential ergodicity of mirror-Langevin diffusions.

Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet, Austin J. Stromme

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence.

Sinho Chewi, Thibaut Le Gouic, Chen Lu, Tyler Maunu, Philippe Rigollet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Gradient descent algorithms for Bures-Wasserstein barycenters.

Sinho Chewi, Tyler Maunu, Philippe Rigollet, Austin J. Stromme

Proceedings of the Conference on Learning Theory, 2020

2019

Matching Observations to Distributions: Efficient Estimation via Sparsified Hungarian Algorithm.

Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, Kannan Ramchandran

Proceedings of the 57th Annual Allerton Conference on Communication, 2019

2018

Online Absolute Ranking with Partial Information: A Bipartite Graph Matching Approach.

Sinho Chewi, Forest Yang, Avishek Ghosh, Abhay Parekh, Kannan Ramchandran

CoRR, 2018

A Combinatorial Proof of a Formula of Biane and Chapuy.

Sinho Chewi, Venkat Anantharam

Electron. J. Comb., 2018