Information-based objective functions for active data selection
Neural Computation
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
Active learning for directed exploration of complex systems
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Centralized modeling of the communication space for spectral awareness in cognitive radio networks
ACM SIGMOBILE Mobile Computing and Communications Review
Globally Optimal Multi-agent Reinforcement Learning Parameters in Distributed Task Assignment
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Evolutionary Model Type Selection for Global Surrogate Modeling
The Journal of Machine Learning Research
Protecting SLAs with surrogate models
Proceedings of the 2nd International Workshop on Principles of Engineering Service-Oriented Systems
Self-Avoiding Random Dynamics on Integer Complex Systems
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
| Hi-index | 0.00 |
Computer experiments often require dense sweeps over input parameters to obtain a qualitative understanding of their response. Such sweeps can be prohibitively expensive, and are unnecessary in regions where the response is easy predicted; well-chosen designs could allow a mapping of the response with far fewer simulation runs. Thus, there is a need for computationally inexpensive surrogate models and an accompanying method for selecting small designs. We explore a general methodology for addressing this need that uses non-stationary Gaussian processes. Binary trees partition the input space to facilitate non-stationarity and a Bayesian interpretation provides an explicit measure of predictive uncertainty that can be used to guide sampling. Our methods are illustrated on several examples, including a motivating example involving computational fluid dynamics simulation of a NASA reentry vehicle.