# Research

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]

#### Software:

- Libra toolkit --
Exact and approximate inference for BNs and MNs,
BN structure learning, and more.
- NBE --
Efficient probability estimation using mixture models.

### 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).