Summary of Accelerated Stochastic Min-max Optimization Based on Bias-corrected Momentum, by Haoyuan Cai et al.
Accelerated Stochastic Min-Max Optimization Based on Bias-corrected Momentum
by Haoyuan Cai, Sulaiman A. Alghunaim, Ali H.Sayed
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes novel bias-corrected momentum algorithms for nonconvex strongly-concave minimax optimization problems, which require efficient Hessian-vector products. By introducing these algorithms, the authors achieve a lower iteration complexity of O(ε^-3) and establish convergence conditions. The effectiveness of this method is demonstrated through applications to robust logistic regression using real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper finds ways to make complex computer programs run faster by solving difficult math problems. It’s like trying to find the shortest path in a maze – the program tries different directions until it gets close enough to the solution. This new method is better than previous ones and can be used to solve real-world problems. |
Keywords
* Artificial intelligence * Logistic regression * Optimization
