A Robust Algorithm for Non-IID Machine Learning Problems with Convergence Analysis

TL;DR

This paper introduces a robust numerical algorithm for non-IID machine learning problems, offering convergence guarantees and broad applicability in fields like robust optimization and imbalanced learning.

Contribution

It presents a novel algorithm combining nonsmooth optimization, quadratic programming, and iterative methods with proven convergence under mild assumptions.

Findings

Algorithm converges under mild conditions

Applicable to robust optimization and imbalanced learning

Provides theoretical convergence proof

Abstract

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.