Search Results - Machine learning Computational learning theory. Submodular functions. Combinatorial optimization.

Greedy Guarantees for Non-submodular Function Maximization Under Independent System Constraint with Applications by Shi, Majun, Yang, Zishen, Wang, Wei

“… These problems often occur in the context of combinatorial optimization, operations research, economics and especially, machine learning and data science…”
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On streaming algorithms for maximizing a supermodular function plus a MDR-submodular function on the integer lattice by Tan, Jingjing, Xu, Yicheng, Zhang, Dongmei, Zhang, Xiaoqing

“…In this paper, we provide a streaming algorithm for the problem of maximizing the sum of a supermodular function and a nonnegative monotone diminishing return submodular (MDR-submodular…”
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Learning with Submodular Functions: A Convex Optimization Perspective by Bach, Francis

“…Submodular functions are relevant to machine learning for at least two reasons: (1) some problems may be expressed directly as the optimization of submodular functions…”
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Provable Non-Convex Optimization and Algorithm Validation via Submodularity by Yatao An Bian

“…Submodularity is one of the most well-studied properties of problem classes in combinatorial optimization and many applications of machine learning and data mining, with strong implications for guaranteed optimization…”
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Randomized Kernel Rounding for Routing, Scheduling, and Machine Learning by Farhadi, Majid

“…From Computer Science, Machine Learning, Communications, and Control Systems to Supply Chain, Finance, and Policy Making we make a sequence of choices, optimality of which can…”
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Graph Theoretic Algorithms Adaptable to Quantum Computing by Srivastava, Siddhartha

“…Computational methods are rapidly emerging as an essential tool for understanding and solving complex engineering problems, which complement the traditional tools of experimentation and theory…”
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