In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.

[EX Q]: Determinantal Point Processes (Dpps) are elegant probabilistic models of repulsion and diversity over discrete sets of items. But their applicability to large sets is hindered by expensive cubic-complexity matrix operations for basic tasks such as sampling. In light of this, we propose a new method for approximate sampling from discrete k-Dpps. Our method takes advantage of the diversity property of subsets sampled from a Dpp, and proceeds in two stages: first it constructs coresets for the ground set of items; thereafter, it efficiently samples subsets based on the constructed coresets. As opposed to previous approaches, our algorithm aims to minimize the total variation distance to the original distribution. Experiments on both synthetic and real datasets indicate that our sampling algorithm works efficiently on large data sets, and yields more accurate samples than previous approaches.
[EX A]: Efficient Sampling for k-Determinantal Point Processes

[EX Q]: Causality has been recently introduced in databases, to model, characterize and possibly compute causes for query results (answers). Connections between queryanswer causality, consistency-based diagnosis, database repairs (wrt. integrity constraint violations), abductive diagnosis and the view-update problem have been established. In this work we further investigate connections between query-answer causality and abductive diagnosis and the view-update problem. In this context, we also define and investigate the notion of query-answer causality in the presence of integrity constraints.
[EX A]: Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints

[EX Q]: We describe a graphical representation of probabilistic relationships-an alternative to the Bayesian network-called a dependency network. Like a Bayesian network, a depen­ dency network has a graph and a probabil­ ity component. The graph component is a (cyclic) directed graph such that a node's parents render that node independent of all other nodes in the network. The probabil­ ity component consists of the probability of a node given its parents for each node (as in a Bayesian network) . We identify several ba­ sic properties of this representation, and de­ scribe its use in collaborative filtering (the task of predicting preferences) and the visu­ alization of predictive relationships.
[EX A]:
Dependency Networks for Collaborative Filtering and Data Visualization