Abstract
Decentralized online convex optimization (DOCO), as a pivotal computational paradigm in machine learning, has been applied to many critical tasks. However, existing DOCO algorithms, due to their excessive emphasis on the worst-case theoretical performance, appear to be overly cautious in making decisions across all possible cases, especially in real-world applications where the worst cases actually hardly occur. Therefore, these existing approaches typically are limited in performance in practice. To avoid such pessimistic strategies, we propose to study the approach of augmenting DOCO with machine-learned predictions that can guide the decision-making process. We present an overview of the problem along with the preliminary results and outlook in this work.
Original language | English |
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Title of host publication | Proceedings - 2024 IEEE 44th International Conference on Distributed Computing Systems, ICDCS 2024 |
Publisher | IEEE |
Pages | 1460-1461 |
Number of pages | 2 |
ISBN (Electronic) | 9798350386059 |
DOIs | |
Publication status | Published - 23 Jul 2024 |
Event | 44th IEEE International Conference on Distributed Computing Systems, ICDCS 2024 - Jersey City, United States Duration: 23 Jul 2024 → 26 Jul 2024 https://icdcs2024.icdcs.org/ https://icdcs2024.icdcs.org/accepted-papers/ https://ieeexplore.ieee.org/xpl/conhome/10630852/proceeding |
Publication series
Name | Proceedings - International Conference on Distributed Computing Systems |
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ISSN (Print) | 1063-6927 |
ISSN (Electronic) | 2575-8411 |
Conference
Conference | 44th IEEE International Conference on Distributed Computing Systems, ICDCS 2024 |
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Country/Territory | United States |
City | Jersey City |
Period | 23/07/24 → 26/07/24 |
Internet address |
Scopus Subject Areas
- Software
- Hardware and Architecture
- Computer Networks and Communications