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Danny Tarlow Ph.D. Student University of Toronto Dept. of Computer Science Machine Learning Group dtarlow cs toronto edu |
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About
I'm a Ph.D. student in the Machine Learning Group within the Department of Computer Science at the University of Toronto, where I also received my Master's Degree (M.Sc.) in January of 2008. I graduated from Stanford in June of 2006 with a Bachelor's Degree (BS) in Computer Science. My advisor is Richard Zemel, and I also work closely with Brendan Frey.I'm broadly interested in the representation, reasoning and learning needed to apply probabilistic models to hard real-world problems. Practically, I rely on things like message passing algorithms, matrix factorizations, combinatorial optimization algorithms, and nonparametric Bayesian methods to provide motivation for how to approach a problem.
Beyond the practice, I am curious at a high theoretical level about why we choose the models that we do. What tradeoffs and implicit assumptions are we making when we choose a specific problem formulation? What analysis needs to be done so that we can justify or improve these decisions?
Most recently, I have been working on (and learning about) the following areas:
- Discrete approximation algorithms.
- Learning additional structure for collaborative filtering.
- Message passing algorithms for nonparametric Bayesian models.
Peer Reviewed Publications
Unsupervised Learning of Skeletons from Feature Trajectories (2008)with David Ross and Richard Zemel.
European Conference on Computer Vision (ECCV).
[pdf]
Flexible Priors for Exemplar-based Clustering (2008)
with
Richard Zemel and
Brendan Frey.
Uncertainty in Artificial Intelligence (UAI).
[pdf]
[bibtex]
with David Ross and Richard Zemel.
Workshop on Dynamical Vision at International Conference on Computer Vision (WDV-ICCV).
[pdf] [webpage] [bibtex]
Using Combinatorial Optimization within Max-Product Belief Propagation (2007)
with
John Duchi,
Gal Elidan, and
Daphne Koller.
Advances in Neural Information Processing Systems (NIPS).
[pdf]
[bibtex]
Selected Talks
Flexible Priors for Exemplar-based ClusteringUncertainty in Artificial Intelligence, Helsinki, Finland, Summer 2008.
[pdf] [video]
Learning Articulated Skeletons from Motion
CIFAR Summer School on Neural Computation and Adaptive Perception,
Summer 2007.
[pdf]
Using Combinatorial Optimization within Max-Product Belief Propagation
Toronto Machine Learning Group,
Fall 2006.
[ppt] [pdf]
The Role of Features in a Feedback-based Ranking System
Google TechTalk,
Summer 2006.
Partition-based Inference in Markov Networks
with John Duchi.
DAGS,
Spring 2006.
Learning in General Games
with Lee Zen and Ankit Garg.
Stanford Logic Group,
Winter 2005.
[link]
Automated Grading of Logic-based Homework Problems
Stanford Logic Group,
Summer 2004.