@article {bac:jmlr17, title = {Hinge-Loss Markov Random Fields and Probabilistic Soft Logic}, journal = {Journal of Machine Learning Research (JMLR)}, volume = {18}, year = {2017}, pages = {1-67}, abstract = {

A fundamental challenge in developing high-impact machine learning technologies is balancing the need to model rich, structured domains with the ability to scale to big data. Many important problem areas are both richly structured and large scale, from social and biological networks, to knowledge graphs and the Web, to images, video, and natural language. In this paper, we introduce two new formalisms for modeling structured data, and show that they can both capture rich structure and scale to big data. The first, hingeloss Markov random fields (HL-MRFs), is a new kind of probabilistic graphical model that generalizes different approaches to convex inference. We unite three approaches from the randomized algorithms, probabilistic graphical models, and fuzzy logic communities, showing that all three lead to the same inference objective. We then define HL-MRFs by generalizing this unified objective. The second new formalism, probabilistic soft logic (PSL), is a probabilistic programming language that makes HL-MRFs easy to define using a syntax based on first-order logic. We introduce an algorithm for inferring most-probable variable assignments (MAP inference) that is much more scalable than general-purpose convex optimization methods, because it uses message passing to take advantage of sparse dependency structures. We then show how to learn the parameters of HL-MRFs. The learned HL-MRFs are as accurate as analogous discrete models, but much more scalable. Together, these algorithms enable HL-MRFs and PSL to model rich, structured data at scales not previously possible.

}, url = {https://github.com/stephenbach/bach-jmlr17-code}, author = {Bach, Stephen H. and Broecheler, Matthias and Huang, Bert and Lise Getoor} } @article {bach:arxiv15, title = {Hinge-Loss Markov Random Fields and Probabilistic Soft Logic}, journal = {ArXiv:1505.04406 [cs.LG]}, year = {2015}, note = {To reference this work, please cite the JMLR paper.}, author = {Bach, Stephen H. and Broecheler, Matthias and Huang, Bert and Lise Getoor} } @conference {broecheler:nips10, title = {Computing marginal distributions over continuous Markov networks for statistical relational learning}, booktitle = {Advances in Neural Information Processing Systems (NIPS)}, year = {2010}, author = {Broecheler, Matthias and Lise Getoor} } @conference {bach:pmpm10, title = {Decision-Driven Models with Probabilistic Soft Logic}, booktitle = {NIPS Workshop on Predictive Models in Personalized Medicine}, year = {2010}, author = {Bach, Stephen H. and Broecheler, Matthias and Kok, Stanley and Lise Getoor} } @conference {broecheler:uai10, title = {Probabilistic Similarity Logic}, booktitle = {Conference on Uncertainty in Artificial Intelligence}, year = {2010}, author = {Broecheler, Matthias and Mihalkova, Lilyana and Lise Getoor} } @conference {broecheler:srl09, title = {Probabilistic Similarity Logic}, booktitle = {International Workshop on Statistical Relational Learning (SRL{\textquoteright}09)}, year = {2009}, author = {Broecheler, Matthias and Lise Getoor} }