I am interested in learning and inference with probabilistic graphical models, especially combining learning and inference with arithmetic circuits and combining first-order logic with uncertainty in statistical relational AI. I have also worked on desktop activity recognition, spam filtering, and recommender systems.

Learning for Efficient Inference

Inference in Bayesian networks and Markov networks is intractable in general, but many special cases are tractable. Often, a tractable subclass such as naive Bayes mixture models yields comparable accuracy while offering exponentially faster inference (Lowd and Domingos, 2005 [pdf] [ppt] [appendix]). Furthermore, by incorporating a preference for tractable models into the learning algorithm, we can guarantee efficient inference without restricting ourselves to any particular class (Lowd and Domingos, 2008 [pdf] [pdf+proofs] [ppt]). Given an intractable model, we can use learning methods to find an accurate but tractable approximation to the original (Lowd and Domingos, 2010 [pdf] [proofs]


Statistical Relational Learning

Statistical relational learning seeks to represent the complexity and uncertainty present in most real-world problems by combining first-order logic with probability. The main challenges are in developing effective representations and effective algorithms. One of my projects has been Recursive Random Fields (RRFs), a multi-layer generalization of Markov logic networks that resolves a number of inconsistencies in the Markov logic representation (Lowd and Domingos, 2007a [pdf] [ppt] [ppt+audio]). I have also worked on applying quadratic optimization algorithms to Markov logic weight learning, resulting in more accurate models in much less time than before (Lowd and Domingos, 2007b [pdf] [ppt] [video]).

Adversarial Machine Learning

I spent the summer of 2004 at Microsoft Research working with Chris Meek on the problem of spam. We looked at a common technique spammers use to defeat filters: adding "good words" to their emails. We developed techniques for evaluating the robustness of spam filters, as well as a theoretical framework for the general problem of learning to defeat a classifier (Lowd and Meek, 2005ab [pdf] [pdf]).

Slides from a talk at Oregon State University (7/14/2006).

Slides from a talk at the 2007 NIPS Workshop on Machine Learning in Adversarial Environments for Computer Security (12/8/2007).

Slides from a talk at the University of Cagliari, Italy (7/3/2008).

Slides from a talk at Portland State University (11/30/2009).