# A comparison study of supervised learning techniques for the approximation of high dimensional functions and feedback control

arXiv: https://arxiv.org/abs/2402.01402 · HTML: https://mlsanity.com/paper/2402.01402/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Feb 2024.

## Built on by (most cited first, 1 of 1)

- [Time-optimal neural feedback control of nilpotent systems as a binary classification problem](https://mlsanity.com/paper/2503.17581/) (2025)

## Abstract (arXiv)

Approximation of high dimensional functions is in the focus of machine learning and data-based scientific computing. In many applications, empirical risk minimisation techniques over nonlinear model classes are employed. Neural networks, kernel methods and tensor decomposition techniques are among the most popular model classes. We provide a numerical study comparing the performance of these methods on various high-dimensional functions with focus on optimal control problems, where the collection of the dataset is based on the application of the State-Dependent Riccati Equation.