Collaborating Authors

Directed Graph Embedding: an Algorithm based on Continuous Limits of Laplacian-type Operators

Neural Information Processing Systems

This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model the observed graph as a sample from a manifold endowed with a vector field, and we design an algo- rithm that separates and recovers the features of this process: the geometry of the manifold, the data density and the vector field. The algorithm is motivated by our analysis of Laplacian-type operators and their continuous limit as generators of diffusions on a manifold. We illustrate the recovery algorithm on both artificially constructed and real data. Papers published at the Neural Information Processing Systems Conference.

Computing Marginal Distributions over Continuous Markov Networks for Statistical Relational Learning

Neural Information Processing Systems

Continuous Markov random fields are a general formalism to model joint probability distributions over events with continuous outcomes. We prove that marginal computation for constrained continuous MRFs is #P-hard in general and present a polynomial-time approximation scheme under mild assumptions on the structure of the random field. Moreover, we introduce a sampling algorithm to compute marginal distributions and develop novel techniques to increase its efficiency. Continuous MRFs are a general purpose probabilistic modeling tool and we demonstrate how they can be applied to statistical relational learning. On the problem of collective classification, we evaluate our algorithm and show that the standard deviation of marginals serves as a useful measure of confidence.

Scaling MPE Inference for Constrained Continuous Markov Random Fields with Consensus Optimization

Neural Information Processing Systems

Probabilistic graphical models are powerful tools for analyzing constrained, continuous domains. However, finding most-probable explanations (MPEs) in these models can be computationally expensive. In this paper, we improve the scalability of MPE inference in a class of graphical models with piecewise-linear and piecewise-quadratic dependencies and linear constraints over continuous domains. We derive algorithms based on a consensus-optimization framework and demonstrate their superior performance over state of the art. We show empirically that in a large-scale voter-preference modeling problem our algorithms scale linearly in the number of dependencies and constraints.

Feral Hog Poison Field Tests in Texas, Alabama in 2018

U.S. News

The bait Vercauteren is working on uses the meat preservative sodium nitrite. It can keep an animal's red blood cells from pulling in oxygen. Pigs make very low levels of an enzyme that counteracts it, so it's more deadly to them than to humans or most domestic animals. Swine that gobble up enough sodium nitrite show symptoms similar to carbon dioxide poisoning: They become uncoordinated, lose consciousness and die within 90 minutes after eating it.

Global Ranking Using Continuous Conditional Random Fields

Neural Information Processing Systems

This paper studies global ranking problem by learning to rank methods. Conventional learning to rank methods are usually designed for local ranking', in the sense that the ranking model is defined on a single object, for example, a document in information retrieval. For many applications, this is a very loose approximation. Relations always exist between objects and it is better to define the ranking model as a function on all the objects to be ranked (i.e., the relations are also included). This paper refers to the problem as global ranking and proposes employing a Continuous Conditional Random Fields (CRF) for conducting the learning task.