A Robust Algorithm for Non-IID Machine Learning Problems with Convergence Analysis
This work addresses robust optimization and imbalanced learning problems, but appears incremental as it builds on existing methods without claiming major breakthroughs.
The authors tackled the problem of solving minimax problems in non-IID machine learning contexts by proposing an improved numerical algorithm based on nonsmooth optimization and quadratic programming, with a rigorous convergence proof under mild assumptions like gradient continuity and boundedness.
In this paper, we propose an improved numerical algorithm for solving minimax problems based on nonsmooth optimization, quadratic programming and iterative process. We also provide a rigorous proof of convergence for our algorithm under some mild assumptions, such as gradient continuity and boundedness. Such an algorithm can be widely applied in various fields such as robust optimization, imbalanced learning, etc.