Query lower bounds for log-concave sampling-Reference-Cited by-同舟云学术

Query lower bounds for log-concave sampling

Published:2023-11-06 Issue: Volume: Page:
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Container-title:2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS)
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Author:

Chewi Sinho¹,De Dios Pont Jaume²,Li Jerry³,Lu Chen⁴,Narayanan Shyam⁵

Affiliation:

1. Institute for Advanced Study,School of Mathematics,Princeton,USA

2. University of California, Los Angeles,Department of Mathematics,Los Angeles,USA

3. Machine Learning Foundations Microsoft Research,Redmond,USA

4. Massachusetts Institute of Technology,Department of Mathematics,Cambridge,USA

5. Massachusetts Institute of Technology,Department of EECS,Cambridge,USA

Funder

Broad Institute

Google

Publisher

IEEE

Link

Reference70 articles.

1. Efficient constrained sampling via the mirror-Langevin algorithm;Ahn;Advances in Neural Information Processing Systems,2021

2. Faster high-accuracy log-concave sampling via algorithmic warm starts;Altschuler;arXiv preprint 2302.10249,2023

3. Resolving the mixing time of the Langevin algorithm to its stationary distribution for log-concave sampling;Altschuler

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2. Krylov Methods are (nearly) Optimal for Low-Rank Approximation;2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS);2023-11-06