# The GroupMax neural network approximation of convex functions

arXiv: https://arxiv.org/abs/2206.06622 · HTML: https://mlsanity.com/paper/2206.06622/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2022.

## Built on by (most cited first, 5 of 5)

- [Input Convex Kolmogorov Arnold Networks](https://mlsanity.com/paper/2505.21208/) (2025)
- [Learning Truthful Mechanisms without Discretization](https://mlsanity.com/paper/2506.22911/) (2025)
- [A new Input Convex Neural Network with application to options pricing](https://mlsanity.com/paper/2411.12854/) (2024)
- [Universal Representation of Generalized Convex Functions and their Gradients](https://mlsanity.com/paper/2509.04477/) (2025)
- [Saddle Networks: Structure-Preserving Architectures for Convex-Concave Functions](https://mlsanity.com/paper/2605.28894/) (2026)

## Abstract (arXiv)

We present a new neural network to approximate convex functions. This network has the particularity to approximate the function with cuts and can be easily adapted to partial convexity. We give an universal approximation theorem in the full convex case and give many numerical results proving it efficiency.