Streaming algorithms are generally judged by the quality of their solution, memory footprint,
and computational complexity. In this paper, we study the problem of maximizing a …

Submodular maximization under various constraints is a fundamental problem studied
continuously, in both computer science and operations research, since the late 1970's. A …

We initiate a systematic study on {\em dynamic influence maximization}(DIM). In the DIM
problem, one maintains a seed set $ S $ of at most $ k $ nodes in a dynamically involving …

Submodular maximization has wide applications in machine learning and data mining,
where massive datasets have brought the great need for designing efficient and …

Submodular maximization is a general optimization problem with a wide range of
applications in machine learning (eg, active learning, clustering, and feature selection). In …

The K-armed dueling bandit problem, where the feedback is in the form of noisy pairwise
comparisons, has been widely studied. Previous works have only focused on the sequential …

In this paper we describe a new parallel algorithm called Fast Adaptive Sequencing
Technique (FAST) for maximizing a monotone submodular function under a cardinality …

Submodular maximization under matroid and cardinality constraints are classical problems
with a wide range of applications in machine learning, auction theory, and combinatorial …

We study the problem of maximizing a non-monotone, non-negative submodular function
subject to a matroid constraint. The prior best-known deterministic approximation ratio for …

In the stochastic submodular cover problem, the goal is to select a subset of stochastic items
of minimum expected cost to cover a submodular function. Solutions in this setting …