Elements of information theory
Elements of information theory
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Near-optimal sensor placements in Gaussian processes
ICML '05 Proceedings of the 22nd international conference on Machine learning
ICML '06 Proceedings of the 23rd international conference on Machine learning
Bayesian treed gaussian process models
Bayesian treed gaussian process models
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
The Journal of Machine Learning Research
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Nonmyopic adaptive informative path planning for multiple robots
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Submodular fractional programming for balanced clustering
Pattern Recognition Letters
Submodularity and its applications in optimized information gathering
ACM Transactions on Intelligent Systems and Technology (TIST)
EvoApplications'11 Proceedings of the 2011 international conference on Applications of evolutionary computation - Volume Part I
Global estimation in constrained environments
International Journal of Robotics Research
In-situ soil moisture sensing: Optimal sensor placement and field estimation
ACM Transactions on Sensor Networks (TOSN)
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Collective inference for network data with copula latent markov networks
Proceedings of the sixth ACM international conference on Web search and data mining
Optimal design for correlated processes with input-dependent noise
Computational Statistics & Data Analysis
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When monitoring spatial phenomena, such as the ecological condition of a river, deciding where to make observations is a challenging task. In these settings, a fundamental question is when an active learning, or sequential design, strategy, where locations are selected based on previous measurements, will perform significantly better than sensing at an a priori specified set of locations. For Gaussian Processes (GPs), which often accurately model spatial phenomena, we present an analysis and efficient algorithms that address this question. Central to our analysis is a theoretical bound which quantifies the performance difference between active and a priori design strategies. We consider GPs with unknown kernel parameters and present a nonmyopic approach for trading off exploration, i.e., decreasing uncertainty about the model parameters, and exploitation, i.e., near-optimally selecting observations when the parameters are (approximately) known. We discuss several exploration strategies, and present logarithmic sample complexity bounds for the exploration phase. We then extend our algorithm to handle nonstationary GPs exploiting local structure in the model. We also present extensive empirical evaluation on several real-world problems.