We propose a novel technique to retrieve itemsets that best explain a transaction database by leveraging a simple probabilistic model. Our approach is the first to infer such interesting itemsets directly from the transaction database using submodular function optimization and in so doing avoids many of the pitfalls commonly present in frequent itemset mining algorithms. Our proposed approach i...

Applications in complex systems such as the Internet have spawned recent interest in studying situations involving multiple agents with their individual cost or utility functions. In this lecture, we introduce an algorithmic framework for studying combinatorial problems in the presence of multiple agents with submodular cost functions. We study several fundamental covering problems (Vertex Cove...

The aim of this paper is to perform Word Sense induction (WSI); which clusters web search results and produces a diversified list of search results. It describes the WSI system developed for Task 11 of SemEval 2013. This paper implements the idea of monotone submodular function optimization using greedy algorithm.

We show that in a large class of semilinear elliptic binary optimal control problems, the point-wise states are submodular functions in the control variables almost everywhere. Moreover, we discuss how to use this result in order to design global optimization algorithms for such problems. To our knowledge, this is the rst time submodularity is investigated in the context of optimal control.

We discuss several recent applications of submodularity to machine learning. First, we present a class of submodular functions useful for document summarization. We show the best ever results on for both generic and query-focused document summarization on widely used and standardized evaluations. We then further improve on these results using a new method to learn submodular mixtures in a large...

The prominence of weakly labeled data gives rise to a growing demand for object detection methods that can cope with minimal supervision. We propose an approach that automatically identifies discriminative configurations of visual patterns that are characteristic of a given object class. We formulate the problem as a constrained submodular optimization problem and demonstrate the benefits of th...

We consider the minimization of submodular functions subject to ordering constraints. We show that this optimization problem can be cast as a convex optimization problem on a space of uni-dimensional measures, with ordering constraints corresponding to first-order stochastic dominance. We propose new discretization schemes that lead to simple and efficient algorithms based on zero-th, first, or...