# Run2Survive: A Decision-theoretic Approach to Algorithm Selection based on Survival Analysis

arXiv: https://arxiv.org/abs/2007.02816 · HTML: https://mlsanity.com/paper/2007.02816/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jul 2020.

## Built on by (most cited first, 8 of 8)

- [A Survey of Methods for Automated Algorithm Configuration](https://mlsanity.com/paper/2202.01651/) (2022)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [Algorithm Selection on a Meta Level](https://mlsanity.com/paper/2107.09414/) (2021)
- [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)
- [Formalizing Preferences Over Runtime Distributions](https://mlsanity.com/paper/2205.13028/) (2022)
- [Utilitarian Algorithm Configuration](https://mlsanity.com/paper/2310.20401/) (2023)

## Abstract (arXiv)

Algorithm selection (AS) deals with the automatic selection of an algorithm from a fixed set of candidate algorithms most suitable for a specific instance of an algorithmic problem class, where "suitability" often refers to an algorithm's runtime. Due to possibly extremely long runtimes of candidate algorithms, training data for algorithm selection models is usually generated under time constraints in the sense that not all algorithms are run to completion on all instances. Thus, training data usually comprises censored information, as the true runtime of algorithms timed out remains unknown. However, many standard AS approaches are not able to handle such information in a proper way. On the other side, survival analysis (SA) naturally supports censored data and offers appropriate ways to use such data for learning distributional models of algorithm runtime, as we demonstrate in this work. We leverage such models as a basis of a sophisticated decision-theoretic approach to algorithm selection, which we dub Run2Survive. Moreover, taking advantage of a framework of this kind, we advocate a risk-averse approach to algorithm selection, in which the avoidance of a timeout is given high priority. In an extensive experimental study with the standard benchmark ASlib, our approach is shown to be highly competitive and in many cases even superior to state-of-the-art AS approaches.