# Research

I am interested in various topics in machine learning, including
adversarial machine learning, learning tractable probabilistic models,
and learning statistical relational models. I have also worked on
desktop activity recognition, spam filtering, and recommender systems.

**(This page is somewhat out of date; see Publications
for more recent work.)**

### 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]). We have new results for
unions and intersections of half-spaces, showing that non-linear
classifiers can also be vulnerable to similar attacks
(

*Stevens and Lowd, 2013*
[

pdf]
[

ppt]).

More recently, I have developed algorithms for learning robust models
for structured prediction. CACC learns collective classification models
that remain effective when some of the features are manipulated
adversarially (*Torkamani and Lowd, 2013* [pdf]). More generally,
we showed that robustness is equivalent to regularization for structured
prediction, so robust optimization can be done efficiently by
constructing an appropriate regularizer (*Torkamani and
Lowd, 2014* [pdf]
[ppt]).

### 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];

*Lowd and Rooshenas,
2013* [

pdf]).
Combining our methods with sum-product network (SPN) learning algorithms,
we obtain the best results for SPN structure learning, often
outperforming intractable Bayesian networks (

*Rooshenas and Lowd,
2014* [

pdf] [

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]).

See Publications for more recent work in
statistical relational learning, co-authored with Shangpu Jiang and Dejing
Dou.