# Auto-Model: Utilizing Research Papers and HPO Techniques to Deal with the CASH problem

arXiv: https://arxiv.org/abs/1910.10902 · HTML: https://mlsanity.com/paper/1910.10902/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2019.

## Built on by (most cited first, 2 of 2)

- [SubStrat: A Subset-Based Strategy for Faster AutoML](https://mlsanity.com/paper/2206.03070/) (2022)
- [Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering](https://mlsanity.com/paper/2512.11984/) (2025)

## Abstract (arXiv)

In many fields, a mass of algorithms with completely different hyperparameters have been developed to address the same type of problems. Choosing the algorithm and hyperparameter setting correctly can promote the overall performance greatly, but users often fail to do so due to the absence of knowledge. How to help users to effectively and quickly select the suitable algorithm and hyperparameter settings for the given task instance is an important research topic nowadays, which is known as the CASH problem. In this paper, we design the Auto-Model approach, which makes full use of known information in the related research paper and introduces hyperparameter optimization techniques, to solve the CASH problem effectively. Auto-Model tremendously reduces the cost of algorithm implementations and hyperparameter configuration space, and thus capable of dealing with the CASH problem efficiently and easily. To demonstrate the benefit of Auto-Model, we compare it with classical Auto-Weka approach. The experimental results show that our proposed approach can provide superior results and achieves better performance in a short time.