Submodular maximization over a matroid constraint is a fundamental problem with various
applications in machine learning. Some of these applications involve decision-making over …

Optimization of DR-submodular functions has experienced a notable surge in significance in
recent times, marking a pivotal development within the domain of non-convex optimization …

Submodular optimization has been identified as a powerful tool for many data mining
applications, where a representative subset of moderate size needs to be extracted from a …

Maximizing monotone submodular functions under cardinality constraints is a classic
optimization task with several applications in data mining and machine learning. In this …

The happiness or regret based query has been another important tool in multi-dimensional
decision-making besides the top-k and skyline queries. To avoid the happiness ratio being …

Submodular function maximization, a crucial aspect of combinatorial optimization, is
extensively applied in various fields like economics and computing. Recently, the focus has …