On Learning Monotone Boolean Functions
FOCS '98 Proceedings of the 39th Annual Symposium on Foundations of Computer Science
Truthful randomized mechanisms for combinatorial auctions
Proceedings of the thirty-eighth annual ACM symposium on Theory of computing
Optimal approximation for the submodular welfare problem in the value oracle model
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Item pricing for revenue maximization
Proceedings of the 9th ACM conference on Electronic commerce
Optimal Cryptographic Hardness of Learning Monotone Functions
ICALP '08 Proceedings of the 35th international colloquium on Automata, Languages and Programming, Part I
Approximating submodular functions everywhere
SODA '09 Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms
Local search for balanced submodular clusterings
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous algorithmic applications that have been used in many areas, including combinatorial optimization, machine learning, and economics. In this work we use a learning theoretic angle for studying submodular functions. We provide algorithms for learning submodular functions, as well as lower bounds on their learnability. In doing so, we uncover several novel structural results revealing both extremal properties as well as regularities of submodular functions, of interest to many areas.