Paper Info
Title
Adaptive batching for Gaussian process surrogates with application in noisy level set estimation
Abstract
We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension of sequential design heuristics with the benefit of replication growing as response features are learned, inputs concentrate, and the metamodeling overhead rises. Motivated by the problem of learning the level set of the mean simulator response, we develop five novel schemes: Multi-Level Batching (MLB), Ratchet Batching (RB), Adaptive Batched Stepwise Uncertainty Reduction (ABSUR), Adaptive Design with Stepwise Allocation (ADSA), and Deterministic Design with Stepwise Allocation (DDSA). Our algorithms simultaneously (MLB, RB, and ABSUR) or sequentially (ADSA and DDSA) determine the sequential design inputs and the respective number of replicates. Illustrations using synthetic examples and an application in quantitative finance (Bermudan option pricing via Regression Monte Carlo) show that adaptive batching brings significant computational speed-ups with minimal loss of modeling fidelity.
Year
DOI
Venue
2022
10.1002/sam.11556
STATISTICAL ANALYSIS AND DATA MINING
Keywords
DocType
Volume
design of experiments, GP surrogates, level set estimation, stepwise uncertainty reduction, stochastic simulation
Journal
15
Issue
ISSN
Citations 
2
1932-1864
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Lyu Xiong100.34
Ludkovski Mike200.34