# Nonlinear Regression without i.i.d. Assumption

arXiv: https://arxiv.org/abs/1811.09623 · HTML: https://mlsanity.com/paper/1811.09623/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [Improving Empathetic Dialogue Generation by Dynamically Infusing Commonsense Knowledge](https://mlsanity.com/paper/2306.04657/) (2023)
- [M2DQN: A Robust Method for Accelerating Deep Q-learning Network](https://mlsanity.com/paper/2209.07809/) (2022)
- [A Robust Algorithm for Non-IID Machine Learning Problems with Convergence Analysis](https://mlsanity.com/paper/2507.00810/) (2025)

## Abstract (arXiv)

In this paper, we consider a class of nonlinear regression problems without the assumption of being independent and identically distributed. We propose a correspondent mini-max problem for nonlinear regression and give a numerical algorithm. Such an algorithm can be applied in regression and machine learning problems, and yield better results than traditional least square and machine learning methods.