Principled Design of Expected Improvement for Continuous Simulation Optimization
Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design choices consequential: the incumbent to define improvement, replication allocation, and final recommendation. Existing methods typically either assume the noise levels are known or choose these components separately through heuristic rules or empirical calibration. We instead derive a high-probability upper bound on the simple regret and systematically choose the three components within general rule classes to tighten this bound. We first establish a finite-sample confidence bound for the SK predictor as a foundati...
| Authors | Songhao Wang, Jianglin Xia, Haowei Wang et al. |
|---|---|
| Primary category | stat.ML |
| Published | 13 October 2026 |
| Last updated at source | 14 October 2026 |
Recent changes
- 15 Oct 2026 Name: Convergence-Rate-Guided Algorithm Design of Expected Improvement for Continuous Simulation Optimization → Principled Design of Expected Improvement for Continuous Simulation Optimization
- 15 Oct 2026 Description: Expected improvement (EI) based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise on simulation output makes three basic design choices consequential: the incumbent → Expected improvement based on stochastic kriging (SK) surrogate is widely used in continuous simulation optimization, but unknown heteroscedastic noise makes three basic design choices consequential: the incumbent to define improvement, rep
Changes detected by Silytics between refreshes of the source.
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 15 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.