This research paper was presented at the 64 th IEEE Symposium on Foundations of Computer Science (FOCS) 2023 (opens in new tab), a premier forum for the latest research in theoretical computer science ...

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

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 ...

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, ...

In the submodular cover problem, we are given a monotone submodular function $f: 2^N \to \mathbb{R}_+$, and we want to pick the min-cost set $S$ such that $f(S) = f(N ...

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 ...

We propose a new objective function for clustering. This objective function consists of two components: the entropy rate of a random walk on a graph and a balancing term. The entropy rate favors ...

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cgq-streaming-greedy is a web service implementing a greedy Streaming Algorithm for Submodular Function Maximization called Streaming Greedy. It consists of the following architecture: In this diagram ...