Abstract
Randomized coordinate descent (RCD) is a popular optimization algorithm with wide applications in solving various machine learning problems, which motivates a lot of theoretical analysis on its convergence behavior. As a comparison, there is no work studying how the models trained by RCD would generalize to test examples. In this paper, we initialize the generalization analysis of RCD by leveraging the powerful tool of algorithmic stability. We establish argument stability bounds of RCD for both convex and strongly convex objectives, from which we develop optimal generalization bounds by showing how to early-stop the algorithm to tradeoff the estimation and optimization. Our analysis shows that RCD enjoys better stability as compared to stochastic gradient descent.
Original language | English |
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Title of host publication | Proceedings of the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 |
Editors | Zhi-Hua Zhou |
Publisher | International Joint Conferences on Artificial Intelligence |
Pages | 3104-3110 |
Number of pages | 7 |
ISBN (Electronic) | 9780999241196 |
DOIs | |
Publication status | Published - Aug 2021 |
Event | 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 - Virtual, Online, Canada Duration: 19 Aug 2021 → 27 Aug 2021 https://ijcai-21.org/# https://www.ijcai.org/proceedings/2021/ |
Publication series
Name | IJCAI International Joint Conference on Artificial Intelligence |
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ISSN (Print) | 1045-0823 |
Conference
Conference | 30th International Joint Conference on Artificial Intelligence, IJCAI 2021 |
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Country/Territory | Canada |
City | Virtual, Online |
Period | 19/08/21 → 27/08/21 |
Internet address |
Scopus Subject Areas
- Artificial Intelligence