Jiawei Han

TL;DR: This paper proposes to consolidate a classification solution by maximizing the consensus among both supervised predictions and unsupervised constraints, and casts this ensemble task as an optimization problem on a bipartite graph, where the objective function favors the smoothness of the predictions over the graph, but penalizes the deviations from the initial labeling provided by the supervised models.

TL;DR: This paper develops ClusChurn, a systematic two-step framework for interpretable new user clustering and churn prediction, based on the intuition that properuser clustering can help understand and predict user churn.

TL;DR: This work proposes a probabilistic graphical model, which simultaneously infers truth as well as source quality without any a priori training involving ground truth answers, and proposes an initialization scheme based upon a quantity named truth existence score, which synthesizes two indicators, namely, participation rate and consistency rate.

TL;DR: A novel knowledge base (KB)-driven tree-structured long short-term memory networks (Tree-LSTM) framework is proposed, incorporating two new types of features: dependency structures to capture wide contexts and entity properties from external ontologies via entity linking.

TL;DR: A novel Entity Topic Model (ETM) is introduced, which not only models the generative process of a term given its topic and entity information, but also models the correlation of entity term distributions and topic term distributions.