Information-based objective functions for active data selection
Neural Computation
Neural network exploration using optimal experiment design
Neural Networks
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
Information, Prediction, and Query by Committee
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Gaussian Process Regression: Active Data Selection and Test Point Rejection
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Accurate and efficient regression modeling for microarchitectural performance and power prediction
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
Efficiently exploring architectural design spaces via predictive modeling
Proceedings of the 12th international conference on Architectural support for programming languages and operating systems
SIGMETRICS '08 Proceedings of the 2008 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Active learning for class probability estimation and ranking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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Computer experiments have become increasingly important in several different industries. These experiments save resources by exploring different designs without necessitating real hardware manufacturing. However, computer experiments usually require lengthy simulation times and powerful computational capacity. Therefore, it is often pragmatically impossible to run experiments on a complete design space. In this paper, we propose an adaptive sampling scheme that interactively works with predictive models to sequentially select design points for computer experiments. The selected samples are used to build predictive models, which in turn guide further sampling and predict the entire design space. For illustration, we use Bayesian additive regression trees (BART), multiple additive regression trees (MART), treed Gaussian process and Gaussian process to guide the proposed sampling method. Both real data and simulation studies show that our sampling method is effective in that (i) it can be used with different predictive models; (ii) it can select multiple design points without repeatedly refitting the predictive models, which makes parallel simulations possible and (iii) the predictive model built on its generated samples gives more accurate predictions on the unsampled points than the models built on samples from other methods such as random sampling, space-filling designs and some adaptive sampling methods. © 2012 Wiley Periodicals, Inc. Statistical Analysis and Data Mining, 2012 © 2012 Wiley Periodicals, Inc.