Active Learning Is Planning: Nonmyopic ε-Bayes-Optimal Active Learning of Gaussian Processes
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Mohan S. Kankanhalli | Kian Hsiang Low | Patrick Jaillet | Trong Nghia Hoang | K. H. Low | Patrick Jaillet | M. Kankanhalli | T. Hoang
[1] Kian Hsiang Low,et al. Active Markov information-theoretic path planning for robotic environmental sensing , 2011, AAMAS.
[2] Kian Hsiang Low,et al. Decentralized active robotic exploration and mapping for probabilistic field classification in environmental sensing , 2012, AAMAS.
[3] Kian Hsiang Low,et al. Gaussian Process-Based Decentralized Data Fusion and Active Sensing for Mobility-on-Demand System , 2013, Robotics: Science and Systems.
[4] Kian Hsiang Low,et al. Information-Theoretic Approach to Efficient Adaptive Path Planning for Mobile Robotic Environmental Sensing , 2009, ICAPS.
[5] Herbert Solomon,et al. Optimal Design of Sampling from Finite Populations: A Critical Review and Indication of New Research Areas , 1970 .
[6] Kian Hsiang Low,et al. A General Framework for Interacting Bayes-Optimally with Self-Interested Agents using Arbitrary Parametric Model and Model Prior , 2013, IJCAI.
[7] Kian Hsiang Low,et al. Adaptive multi-robot wide-area exploration and mapping , 2008, AAMAS.
[8] Alberto Elfes,et al. Cooperative aquatic sensing using the telesupervised adaptive ocean sensor fleet , 2009, Remote Sensing.
[9] Andreas Krause,et al. Efficient Informative Sensing using Multiple Robots , 2014, J. Artif. Intell. Res..
[10] Kian Hsiang Low,et al. Parallel Gaussian Process Regression with Low-Rank Covariance Matrix Approximations , 2013, UAI.
[11] Andreas Krause,et al. Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..
[12] Zoubin Ghahramani,et al. Collaborative Gaussian Processes for Preference Learning , 2012, NIPS.
[13] Peter J. Diggle,et al. Bayesian Geostatistical Design , 2006 .
[14] Jon Lee. Maximum entropy sampling , 2001 .
[15] Mijung Park,et al. Bayesian active learning with localized priors for fast receptive field characterization , 2012, NIPS.
[16] R. J. Martin. Comparing and contrasting some environmental and experimental design problems , 2001 .
[17] Kian Hsiang Low,et al. Adaptive Sampling for Multi-Robot Wide-Area Exploration , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.
[18] Kian Hsiang Low,et al. Multi-robot active sensing of non-stationary gaussian process-based environmental phenomena , 2014, AAMAS.
[19] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[20] Jesse Hoey,et al. An analytic solution to discrete Bayesian reinforcement learning , 2006, ICML.
[21] Mohan S. Kankanhalli,et al. Nonmyopic \(\epsilon\)-Bayes-Optimal Active Learning of Gaussian Processes , 2014, ICML.
[22] Gaurav S. Sukhatme,et al. Decentralized Data Fusion and Active Sensing with Mobile Sensors for Modeling and Predicting Spatiotemporal Traffic Phenomena , 2012, UAI.
[23] Thomas M. Cover,et al. Elements of Information Theory , 2005 .
[24] Dale L. Zimmerman,et al. Optimal network design for spatial prediction, covariance parameter estimation, and empirical prediction , 2006 .
[25] Andreas Krause,et al. Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach , 2007, ICML '07.
[26] Werner G. Müller,et al. Collecting Spatial Data: Optimum Design of Experiments for Random Fields , 1998 .
[27] Kian Hsiang Low,et al. Multi-robot informative path planning for active sensing of environmental phenomena: a tale of two algorithms , 2013, AAMAS.
[28] Kian Hsiang Low,et al. Telesupervised remote surface water quality sensing , 2010, 2010 IEEE Aerospace Conference.