Elements of information theory
Elements of information theory
Information-based objective functions for active data selection
Neural Computation
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
A randomized art-gallery algorithm for sensor placement
SCG '01 Proceedings of the seventeenth annual symposium on Computational geometry
Connected sensor cover: self-organization of sensor networks for efficient query execution
Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Nonmyopic active learning of Gaussian processes: an exploration-exploitation approach
Proceedings of the 24th international conference on Machine learning
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Robust, low-cost, non-intrusive sensing and recognition of seated postures
Proceedings of the 20th annual ACM symposium on User interface software and technology
Algorithms for subset selection in linear regression
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Turning down the noise in the blogosphere
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Simultaneous placement and scheduling of sensors
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Active learning with statistical models
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
SFO: A Toolbox for Submodular Function Optimization
The Journal of Machine Learning Research
Online distributed sensor selection
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
Nonmyopic Multiaspect Sensing With Partially Observable Markov Decision Processes
IEEE Transactions on Signal Processing
Online submodular minimization
The Journal of Machine Learning Research
Hi-index | 0.00 |
Where should we place sensors to efficiently monitor natural drinking water resources for contamination? Which blogs should we read to learn about the biggest stories on the Web? These problems share a fundamental challenge: How can we obtain the most useful information about the state of the world, at minimum cost? Such information gathering, or active learning, problems are typically NP-hard, and were commonly addressed using heuristics without theoretical guarantees about the solution quality. In this article, we describe algorithms which efficiently find provably near-optimal solutions to large, complex information gathering problems. Our algorithms exploit submodularity, an intuitive notion of diminishing returns common to many sensing problems: the more sensors we have already deployed, the less we learn by placing another sensor. In addition to identifying the most informative sensing locations, our algorithms can handle more challenging settings, where sensors need to be able to reliably communicate over lossy links, where mobile robots are used for collecting data, or where solutions need to be robust against adversaries and sensor failures. We also present results applying our algorithms to several real-world sensing tasks, including environmental monitoring using robotic sensors, activity recognition using a built sensing chair, a sensor placement challenge, and deciding which blogs to read on the Web.