Abstract: Submodular maximization enables efficient approximation of machine learning, networking, and language processing problems. Typically, these problems have been shown to have matroid ...

Submodular optimisation lies at the heart of a broad class of combinatorial decision problems in which the objective exhibits diminishing marginal returns. Formally, a submodular function assigns a ...

Abstract: We introduce several generalizations of classical computer science problems obtained by replacing simpler objective functions with general submodular functions.The new problems include ...

In this paper, we propose a framework of maximizing quadratic submodular energy with a knapsack constraint approximately, to solve certain computer vision problems. The proposed submodular ...

We consider learning of submodular functions from data. These functions are important in machine learning and have a wide range of applications, e.g. data summarization, feature selection and active ...

A k-submodular function is a generalization of a submodular function, where the input consists of k disjoint subsets, instead of a single subset, of the domain. Many machine learning problems, ...

The study of combinatorial problems with a submodular objective function has attracted much attention in recent years, and is partly motivated by the importance of such problems to economics, ...

This repository provides a python implementation of our AISTATS 2020 paper titled ''Adaptive Sampling for Fast Constrained Maximization of Submodular Functions''. Several large-scale machine learning ...

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