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

arXiv: https://arxiv.org/abs/2507.00810 · HTML: https://mlsanity.com/paper/2507.00810/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 6 references that are in the map.
- First posted on arXiv 1 Jul 2025.

## Builds on (most cited first, 2 of 2)

- [Towards Out-Of-Distribution Generalization: A Survey](https://mlsanity.com/paper/2108.13624/) (2021)
- [Nonlinear Regression without i.i.d. Assumption](https://mlsanity.com/paper/1811.09623/) (2018)

## Abstract (arXiv)

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.