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Sampling and integration of near log-concave functions

Authors:

David Applegate,

Ravi KannanAuthors Info & Claims

STOC '91: Proceedings of the twenty-third annual ACM symposium on Theory of Computing

Pages 156 - 163

https://doi.org/10.1145/103418.103439

Published: 03 January 1991 Publication History

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McDonald CBarron A(2024)Log-Concave Coupling for Sampling Neural Net Posteriors2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619407(2251-2256)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ISIT57864.2024.10619407
Fishman NKlarner LMathieu EDe Bortoli VHutchinson MOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Metropolis sampling for constrained diffusion modelsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668843(62296-62331)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668843
Balcan MHanneke SPukdee RSharma DOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Reliable learning in challenging environmentsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668205(48035-48050)Online publication date: 10-Dec-2023
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Index Terms

Sampling and integration of near log-concave functions
1. Mathematics of computing
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis

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We propose a computationally efficient random walk on a convex body which rapidly mixes with respect to a fixed log-concave distribution and closely tracks a time-varying log-concave distribution. We develop general theoretical guarantees on the ...

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Published In

STOC '91: Proceedings of the twenty-third annual ACM symposium on Theory of Computing

January 1991

572 pages

ISBN:0897913973

DOI:10.1145/103418

Chairmen:
Cris Koutsougeras
Tulane Univ., New Orleans, LA
,
Jeff Vitter
Brown Univ., Providence, RI

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 03 January 1991

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STOC91: 23rd Annual ACM Symposium on Theory of Computing

May 5 - 8, 1991

Louisiana, New Orleans, USA

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Overall Acceptance Rate 1,469 of 4,586 submissions, 32%

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McDonald CBarron A(2024)Log-Concave Coupling for Sampling Neural Net Posteriors2024 IEEE International Symposium on Information Theory (ISIT)10.1109/ISIT57864.2024.10619407(2251-2256)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ISIT57864.2024.10619407
Fishman NKlarner LMathieu EDe Bortoli VHutchinson MOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Metropolis sampling for constrained diffusion modelsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668843(62296-62331)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668843
Balcan MHanneke SPukdee RSharma DOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Reliable learning in challenging environmentsProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668205(48035-48050)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668205
Mangoubi OVishnoi NOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Sampling from structured log-concave distributions via a soft-threshold dikin walkProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667507(31908-31942)Online publication date: 10-Dec-2023
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Narayanan HRajaraman ASrivastava PSaha BServedio R(2023)Sampling from Convex Sets with a Cold Start using Multiscale DecompositionsProceedings of the 55th Annual ACM Symposium on Theory of Computing10.1145/3564246.3585172(117-130)Online publication date: 2-Jun-2023
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Vempala SWibisono A(2023)Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry SufficesGeometric Aspects of Functional Analysis10.1007/978-3-031-26300-2_15(381-438)Online publication date: 30-Sep-2023
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Mangoubi OVishnoi NKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Sampling from log-concave distributions with infinity-distance guaranteesProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601188(12633-12646)Online publication date: 28-Nov-2022
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Abboud RCeylan İDimitrov R(2022)Approximate weighted model integration on DNF structuresArtificial Intelligence10.1016/j.artint.2022.103753311(103753)Online publication date: Oct-2022
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De AKhanna SLi HNikpey HRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Approximate optimization of convex functions with outlier noiseProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3540884(8147-8157)Online publication date: 6-Dec-2021
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Jia HLaddha ALee YVempala SKhuller SVassilevska Williams V(2021)Reducing isotropy and volume to KLS: an o*(n3ψ2) volume algorithmProceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing10.1145/3406325.3451018(961-974)Online publication date: 15-Jun-2021
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Sampling from a Log-Concave Distribution with Projected Langevin Monte Carlo

Sampling, integration, and computing volumes

Efficient sampling from time-varying log-concave distributions

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Sampling from a Log-Concave Distribution with Projected Langevin Monte Carlo

Sampling, integration, and computing volumes

Efficient sampling from time-varying log-concave distributions

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