A combinatorial algorithm for minimizing symmetric submodular functions
Proceedings of the sixth annual ACM-SIAM symposium on Discrete algorithms
The Data-Correcting Algorithm for the Minimization of Supermodular Functions
Management Science
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IEEE Transactions on Pattern Analysis and Machine Intelligence
PAC-learning bounded tree-width graphical models
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Greedy splitting algorithms for approximating multiway partition problems
Mathematical Programming: Series A and B
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
Cost-effective outbreak detection in networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Maximizing Non-Monotone Submodular Functions
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
The Journal of Machine Learning Research
Simultaneous placement and scheduling of sensors
IPSN '09 Proceedings of the 2009 International Conference on Information Processing in Sensor Networks
Submodularity and its applications in optimized information gathering
ACM Transactions on Intelligent Systems and Technology (TIST)
Branch and bound strategies for non-maximal suppression in object detection
EMMCVPR'11 Proceedings of the 8th international conference on Energy minimization methods in computer vision and pattern recognition
Optimal experimental design for sampling voltage on dendritic trees in the low-SNR regime
Journal of Computational Neuroscience
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In recent years, a fundamental problem structure has emerged as very useful in a variety of machine learning applications: Submodularity is an intuitive diminishing returns property, stating that adding an element to a smaller set helps more than adding it to a larger set. Similarly to convexity, submodularity allows one to efficiently find provably (near-) optimal solutions for large problems. We present SFO, a toolbox for use in MATLAB or Octave that implements algorithms for minimization and maximization of submodular functions. A tutorial script illustrates the application of submodularity to machine learning and AI problems such as feature selection, clustering, inference and optimized information gathering.