# Efficient Learning for Clustering and Optimizing Context-Dependent Designs

arXiv: https://arxiv.org/abs/2012.05591 · HTML: https://mlsanity.com/paper/2012.05591/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Learning to Simulate: Generative Metamodeling via Quantile Regression](https://mlsanity.com/paper/2311.17797/) (2023)
- [An Agglomerative Clustering of Simulation Output Distributions Using Regularized Wasserstein Distance](https://mlsanity.com/paper/2407.12100/) (2024)
- [Efficient Learning for Selecting Top-m Context-Dependent Designs](https://mlsanity.com/paper/2305.04086/) (2023)
- [Series Expansion of Probability of Correct Selection for Improved Finite Budget Allocation in Ranking and Selection](https://mlsanity.com/paper/2411.10695/) (2024)

## Abstract (arXiv)

We consider a simulation optimization problem for a context-dependent decision-making. A Gaussian mixture model is proposed to capture the performance clustering phenomena of context-dependent designs. Under a Bayesian framework, we develop a dynamic sampling policy to efficiently learn both the global information of each cluster and local information of each design for selecting the best designs in all contexts. The proposed sampling policy is proved to be consistent and achieve the asymptotically optimal sampling ratio. Numerical experiments show that the proposed sampling policy significantly improves the efficiency in context-dependent simulation optimization.