2016 NIPS NeurIPS 2016

Maximization of Approximately Submodular Functions

Abstract

We study the problem of maximizing a function that is approximately submodular under a cardinality constraint. Approximate submodularity implicitly appears in a wide range of applications as in many cases errors in evaluation of a submodular function break submodularity. Say that $F$ is $\eps$-approximately submodular if there exists a submodular function $f$ such that $(1-\eps)f(S) \leq F(S)\leq (1+\eps)f(S)$ for all subsets $S$. We are interested in characterizing the query-complexity of maximizing $F$ subject to a cardinality constraint $k$ as a function of the error level $\eps > 0$. We provide both lower and upper bounds: for $\eps > n^{-1/2}$ we show an exponential query-complexity lower bound. In contrast, when $\eps < {1}/{k}$ or under a stronger bounded curvature assumption, we give constant approximation algorithms.

🌉 Interdisciplinary Bridge — Machine Learning and Mathematics & Optimization
📈 Trend Setter — Approximation Algorithms
🧭 Keyword Pioneer — approximate submodularity
🐣 Hot Topic Early Bird — submodular optimization
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio