Robust Submodular Maximization: A Non-Uniform Partitioning Approach


We study the problem of maximizing a monotone submodular function subject to a cardinality constraint $k$, with the added twist that a number of items $\tau$ from the returned set may be removed. We focus on the worst-case setting considered in Orlin et al. 2016, in which a constant-factor approximation guarantee was given for $\tau = o(\sqrt{k})$. In this paper, we solve a key open problem raised therein, presenting a new Partitioned Robust (PRO) submodular maximization algorithm that achieves the same guarantee for more general $\tau = o(k)$. Our algorithm constructs partitions consisting of buckets with exponentially increasing sizes, and applies standard submodular optimization subroutines on the buckets in order to construct the robust solution. We numerically demonstrate the performance of PRO in data summarization and influence maximization, demonstrating gains over both the greedy algorithm and the algorithm of Orlin et al. 2016.

International Conference on Machine Learning (ICML), Sydney, 2017