5 accepted papers
In this work, we propose the first constant-approximation algorithms, $\mathsf{LinAst}$ and $\mathsf{LinAtg}$, which simultaneously achieve optimal query complexity $O(n)$ and adaptive complexity $O(\log n)$ for non-monotone submodular maximization under a cardinality constraint $k$ over a ground se
Fairness k-submodular maximization has attracted increasing interest due to its broad relevance in artificial intelligence and machine learning. However, most existing work is limited to monotone objectives or simple size constraints, while the non-monotone setting with richer constraints remains la
We study the Quality of Service Degradation (QoSD) problem, in which an adversary perturbs edge weights to degrade network performance. This setting arises in both network infrastructures and distributed ML systems, where communication quality, not just connectivity, determines functionality. While…
This work proposes an efficient parallel algorithm for non-monotone submodular maximization under a knapsack constraint problem over the ground set of size n. Our algorithm improves the best approximation factor of the existing parallel one from 8 to 7 with O(log n) adaptive complexity. The ke…
This work, for the first time, introduces two constant factor approximation algorithms with linear query complexity for non-monotone submodular maximization over a ground set of size n subject to a knapsack constraint, DLA and RLA. DLA is a deterministic algorithm that provides an approximation fac…