# Context-dependent Ranking and Selection under a Bayesian Framework

arXiv: https://arxiv.org/abs/2012.05577 · HTML: https://mlsanity.com/paper/2012.05577/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

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

- [Selecting the Best Optimizing System](https://mlsanity.com/paper/2201.03065/) (2022)
- [Selection of the Most Probable Best](https://mlsanity.com/paper/2207.07533/) (2022)

## Abstract (arXiv)

We consider a context-dependent ranking and selection problem. The best design is not universal but depends on the contexts. Under a Bayesian framework, we develop a dynamic sampling scheme for context-dependent optimization (DSCO) to efficiently learn and select the best designs in all contexts. The proposed sampling scheme is proved to be consistent. Numerical experiments show that the proposed sampling scheme significantly improves the efficiency in context-dependent ranking and selection.