Daniel Lowd

I am a fifth-year graduate student in Computer Science and Engineering at the University of Washington. In the picture above, I'm the one wearing glasses.

Contact Info

E-mail: lowd at cs dot washington dot edu
Office: 430 Allen Center

Address:
Paul Allen Center for Computer Science and Engineering, Room 430
Mailstop 352350
University of Washington
Seattle, WA, 98195-2350

Research

My focus is on statistical relational learning, but I'm also interested in recommender systems, spam filtering, and machine learning in general. I am currently working with Pedro Domingos on miscellaneous things that are somehow related to Markov logic networks.

Recursive Random Fields

Recursive Random Fields (RRFs) unify first-order logic and probabilistic models by converting each logical connective or quantifier into a probability distribution. RRFs are analagous to multi-layered perceptrons, except that instead of generalizing propositional logic for classification, RRFs generalize first-order logic for probability estimation. For more information, read the publications below or listen to a talk I gave at IJCAI.

Naive Bayes Estimation

One of my past projects explored using naive Bayes mixture models for probability estimation. These models tend to offer equivalent accuracy to more general Bayesian networks, but have much more efficient inference. For more information on using naive Bayes models for probability estimation, see the ICML paper below or check out the online appendix, complete with extra graphs, model files, and an open source implementation.

Attacking Spam Filters

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.

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).

Publications

Markov Logic. Pedro Domingos, Stanley Kok, Daniel Lowd, Hoifung Poon, Matthew Richardson, and Parag Singla. In L. De Raedt, P. Frasconi, K. Kersting and S. Muggleton (eds.), Probabilistic Inductive Logic Programming (pp. 92-117), 2008. New York: Springer.
Efficient Weight Learning for Markov Logic Networks. Daniel Lowd and Pedro Domingos. Proceedings of the Eleventh European Conference on Principles and Practices of Knowledge Discovery in Databases (PKDD), 2007. Warsaw, Poland: Springer Verlag. (Slides) (Video)
[Updated PDF file fixes several formula errors.]
Recursive Random Fields. Daniel Lowd and Pedro Domingos. Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, 2007. Hyderabad, India: IJCAI. (Slides) (Slides+Audio)
Recursive Random Fields (workshop version). Daniel Lowd and Pedro Domingos. Proceedings of the ICML-2006 Workshop on Open Problem in Statistical Relational Learning, 2006. Pittsburgh, PA: IMLS. (Slides)
Naive Bayes Models for Probability Estimation. Daniel Lowd and Pedro Domingos. Proceedings of the Twenty-Second International Conference on Machine Learning (ICML), 2005. Bonn, Germany: ACM Press. (Slides) (Online appendix)
Good Word Attacks on Statistical Spam Filters. Daniel Lowd and Christopher Meek. Second Conference on Email and Anti-Spam (CEAS), 2005. Palo Alto, CA. (Slides)
Adversarial Learning. Daniel Lowd and Christopher Meek. Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2005. Chicago, IL: ACM Press. (Poster)

CSE Poetry

Here is a villanelle I wrote for architecture class. I had to write it in order to get a one-week extension on the final project. Writing was quite enjoyable... alas, there is no quals course in poetry!

Fortunately, one of the questions on the architecture final asked me to answer a question of my own creation. I received full credit on it, too.

The following quarter inspired this creation. I was taking advanced complexity at the time.

Other Interests

I sing with the Seattle Men's Chorus.

My wife, Mary Lowd, is a science fiction writer. Buy her book! (Also available through Amazon.com.) Her web page includes some excellent pictures of our daughter and our pets.