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Probabilistic soft logic (PSL) is a framework for collective, probabilistic reasoning in relational domains. PSL uses first order logic rules as a template language for graphical models over random variables with soft truth values from the interval [0, 1]. Inference in this setting is a continuous optimization task, which can be solved efficiently. This(More)
Graphical models for structured domains are powerful tools, but the computational complexities of combinatorial prediction spaces can force restrictions on models, or require approximate inference in order to be tractable. Instead of working in a combinatorial space, we use hinge-loss Markov random fields (HL-MRFs), an expressive class of graphical models(More)
This paper introduces hinge-loss Markov random fields (HL-MRFs), a new class of probabilistic graphical models particularly well-suited to large-scale structured prediction and learning. We derive HL-MRFs by unifying and then generalizing three different approaches to scalable inference in structured models: (1) randomized algorithms for MAX SAT, (2) local(More)
Many real-world networks are described by both connectivity information and features for every node. To better model and understand these networks, we present structure preserving metric learning (SPML), an algorithm for learning a Mahalanobis distance metric from a network such that the learned distances are tied to the inherent connectivity structure of(More)
Online debate forums present a valuable opportunity for the understanding and modeling of dialogue. To understand these debates, a key challenge is inferring the stances of the participants, all of which are interrelated and dependent. While collectively modeling users’ stances has been shown to be effective (Walker et al., 2012c; Hasan and Ng, 2013), there(More)
Massive open online courses (MOOCs) attract a large number of student registrations, but recent studies have shown that only a small fraction of these students complete their courses. Student dropouts are thus a major deterrent for the growth and success of MOOCs. We believe that understanding student engagement as a course progresses is essential for(More)
We propose Semantic Model Vectors, an intermediate level semantic representation, as a basis for modeling and detecting complex events in unconstrained real-world videos, such as those from YouTube. The Semantic Model Vectors are extracted using a set of discriminative semantic classifiers, each being an ensemble of SVM models trained from thousands of(More)
We describe the design, implementation, and evaluation of EMBERS, an automated, 24x7 continuous system for forecasting civil unrest across 10 countries of Latin America using open source indicators such as tweets, news sources, blogs, economic indicators, and other data sources. Unlike retrospective studies, EMBERS has been making forecasts into the future(More)
Power companies can benefit from the use of knowledge discovery methods and statistical machine learning for preventive maintenance. We introduce a general process for transforming historical electrical grid data into models that aim to predict the risk of failures for components and systems. These models can be used directly by power companies to assist(More)