Active Learning of Piecewise Gaussian Process Surrogates
This work addresses the need for efficient data acquisition in applications like autonomous materials design and smart factory systems, but it is incremental as it adapts existing active learning strategies to a specific model type.
The paper tackles the problem of active learning for piecewise Gaussian process surrogates, which are discontinuous across regions, by developing heuristics that account for model bias instead of just uncertainty, and demonstrates advantages on synthetic and real-simulation benchmarks.
Active learning of Gaussian process (GP) surrogates has been useful for optimizing experimental designs for physical/computer simulation experiments, and for steering data acquisition schemes in machine learning. In this paper, we develop a method for active learning of piecewise, Jump GP surrogates. Jump GPs are continuous within, but discontinuous across, regions of a design space, as required for applications spanning autonomous materials design, configuration of smart factory systems, and many others. Although our active learning heuristics are appropriated from strategies originally designed for ordinary GPs, we demonstrate that additionally accounting for model bias, as opposed to the usual model uncertainty, is essential in the Jump GP context. Toward that end, we develop an estimator for bias and variance of Jump GP models. Illustrations, and evidence of the advantage of our proposed methods, are provided on a suite of synthetic benchmarks, and real-simulation experiments of varying complexity.