# Extreme Algorithm Selection With Dyadic Feature Representation

arXiv: https://arxiv.org/abs/2001.10741 · HTML: https://mlsanity.com/paper/2001.10741/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2020.

## Built on by (most cited first, 11 of 11)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Large Language Model-Enhanced Algorithm Selection: Towards Comprehensive Algorithm Representation](https://mlsanity.com/paper/2311.13184/) (2023)
- [Zero-Shot AutoML with Pretrained Models](https://mlsanity.com/paper/2206.08476/) (2022)
- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML](https://mlsanity.com/paper/2307.00285/) (2023)
- [Machine Learning for Online Algorithm Selection under Censored Feedback](https://mlsanity.com/paper/2109.06234/) (2021)
- [HARRIS: Hybrid Ranking and Regression Forests for Algorithm Selection](https://mlsanity.com/paper/2210.17341/) (2022)
- [Automated Machine Learning, Bounded Rationality, and Rational Metareasoning](https://mlsanity.com/paper/2109.04744/) (2021)
- [A Recommender System for Scientific Datasets and Analysis Pipelines](https://mlsanity.com/paper/2108.09275/) (2021)
- [Unlock the Power of Algorithm Features: A Generalization Analysis for Algorithm Selection](https://mlsanity.com/paper/2405.11349/) (2024)
- [Unsupervised Multi-kernel Learning for Automated Algorithm Selection](https://mlsanity.com/paper/2607.19031/) (2026)

## Abstract (arXiv)

Algorithm selection (AS) deals with selecting an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem, e.g., choosing solvers for SAT problems. Benchmark suites for AS usually comprise candidate sets consisting of at most tens of algorithms, whereas in combined algorithm selection and hyperparameter optimization problems the number of candidates becomes intractable, impeding to learn effective meta-models and thus requiring costly online performance evaluations. Therefore, here we propose the setting of extreme algorithm selection (XAS) where we consider fixed sets of thousands of candidate algorithms, facilitating meta learning. We assess the applicability of state-of-the-art AS techniques to the XAS setting and propose approaches leveraging a dyadic feature representation in which both problem instances and algorithms are described. We find the latter to improve significantly over the current state of the art in various metrics.