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Expert-Calibrated Learning for Online Optimization with Switching Costs

Authors:

Pengfei Li,

Jianyi Yang,

Shaolei RenAuthors Info & Claims

SIGMETRICS/PERFORMANCE '22: Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

Pages 85 - 86

https://doi.org/10.1145/3489048.3530961

Published: 06 June 2022 Publication History

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Abstract

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References

[1]

Gautam Goel, Yiheng Lin, Haoyuan Sun, and Adam Wierman. 2019. Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization. In NeurIPS, Vol. 32. https://proceedings.neurips.cc/paper/2019/file/ 9f36407ead0629fc166f14dde7970f68-Paper.pdf

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[2]

Pengfei Li, Jianyi Yang, and Shaolei Ren. 2022. Expert-Calibrated Learning for Online Optimization with Switching Costs. Proc. ACM Meas. Anal. Comput. Syst. 6, 2, Article 28 (Jun 2022), 35 pages.

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Cited By

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Li PYang JWierman ARen S(2024)Learning-Augmented Decentralized Online Convex Optimization in NetworksProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004208:3(1-42)Online publication date: 13-Dec-2024
https://dl.acm.org/doi/10.1145/3700420
Yang JLi PIslam MRen S(2024)Online Allocation with Replenishable Budgets: Worst Case and BeyondProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36390308:1(1-34)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1145/3639030
Yang JLi PLi TWierman ARen SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Anytime-competitive reinforcement learning with policy priorProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669523(77852-77866)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669523
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Index Terms

Expert-Calibrated Learning for Online Optimization with Switching Costs
1. Computing methodologies
  1. Machine learning
  2. Symbolic and algebraic manipulation
    1. Symbolic and algebraic algorithms
      1. Optimization algorithms

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Expert-Calibrated Learning for Online Optimization with Switching Costs
POMACS

We study online convex optimization with switching costs, a practically important but also extremely challenging problem due to the lack of complete offline information. By tapping into the power of machine learning (ML) based optimizers, ML-augmented ...
Expert-Calibrated Learning for Online Optimization with Switching Costs
SIGMETRICS '22
Learning Global Optimization by Deep Reinforcement Learning
Intelligent Systems
Abstract
Learning to Optimize (L2O) is a growing field that employs a variety of machine learning (ML) methods to learn optimization algorithms automatically from data instead of developing hand-engineered algorithms that usually require hyperparameter ...

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Information & Contributors

Information

Published In

SIGMETRICS/PERFORMANCE '22: Abstract Proceedings of the 2022 ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

June 2022

132 pages

ISBN:9781450391412

DOI:10.1145/3489048

General Chairs:
D Manjunath
IIT Bombay, India
,
Jayakrishnan Nair
IIT Bombay, India
,
Program Chairs:
Niklas Carlsson
Linköping University, Sweden
,
Edith Cohen
Google Research, USA
,
Philippe Robert
INRIA, France

ACM SIGMETRICS Performance Evaluation Review Volume 50, Issue 1
SIGMETRICS '22
June 2022
118 pages
ISSN:0163-5999
DOI:10.1145/3547353
Editor:
Zhenhua Liu
Stony Brook University
Issue’s Table of Contents

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2022

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Funding Sources

NSF (National Science Foundation)

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SIGMETRICS/PERFORMANCE '22

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SIGMETRICS

SIGMETRICS/PERFORMANCE '22: ACM SIGMETRICS/IFIP PERFORMANCE Joint International Conference on Measurement and Modeling of Computer Systems

June 6 - 10, 2022

Mumbai, India

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Overall Acceptance Rate 459 of 2,691 submissions, 17%

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Cited By

View all

Li PYang JWierman ARen S(2024)Learning-Augmented Decentralized Online Convex Optimization in NetworksProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/37004208:3(1-42)Online publication date: 13-Dec-2024
https://dl.acm.org/doi/10.1145/3700420
Yang JLi PIslam MRen S(2024)Online Allocation with Replenishable Budgets: Worst Case and BeyondProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/36390308:1(1-34)Online publication date: 21-Feb-2024
https://dl.acm.org/doi/10.1145/3639030
Yang JLi PLi TWierman ARen SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Anytime-competitive reinforcement learning with policy priorProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669523(77852-77866)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669523
Li PYang JWierman ARen SOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Robust learning for smoothed online convex optimization with feedback delayProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666861(16889-16916)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666861
Li PYang JRen S(2023)Robustified Learning for Online Optimization with Memory CostsIEEE INFOCOM 2023 - IEEE Conference on Computer Communications10.1109/INFOCOM53939.2023.10228998(1-10)Online publication date: 17-May-2023
https://doi.org/10.1109/INFOCOM53939.2023.10228998
Li PYang JRen S(2022)Expert-Calibrated Learning for Online Optimization with Switching CostsACM SIGMETRICS Performance Evaluation Review10.1145/3547353.353096150:1(85-86)Online publication date: 7-Jul-2022
https://dl.acm.org/doi/10.1145/3547353.3530961

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