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Daniel Tarlow

Daniel Tarlow
RESEARCHER
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I'm a Researcher in the Machine Learning and Perception group at Microsoft Research Cambridge.

Previously, I was a postdoc here at MSRC, and I did a Ph.D. and M.Sc. at the University of Toronto supervised by Richard Zemel. I hold a B.S. in Computer Science from Stanford University.

If you have a strong background in Machine Learning, have a taste for tackling hard new problems in creative ways, and are interested in an internship, please contact me!

Publications

2015

Varun Jampani, SM Ali Eslami, Daniel Tarlow, Pushmeet Kohli, and John Winn, Consensus Message Passing for Layered Graphical Models, in The 18th International Conference on Artificial Intelligence and Statistics (AISTATS 2015), , May 2015.

2014

Chris J Maddison, Daniel Tarlow, and Tom Minka, A* Sampling, in Advances in Neural Information Processing Systems (NIPS), December 2014.

SM Ali Eslami, Daniel Tarlow, Pushmeet Kohli, and John Winn, Just-In-Time Learning for Fast and Flexible Inference, in Advances in Neural Information Processing Systems (NIPS), December 2014.

Adrian Kim, Kyomin Jung, Daniel Tarlow, and Pushmeet Kohli, Computing Expected Losses in Perturbation Models using Multidimensional Parametric Min-cuts, in NIPS Workshop on Perturbations, Optimization, and Statistics, December 2014.

Andrew D. Gordon, Claudio Russo, Marcin Szymczak, Johannes Borgstrom, Nicolas Rolland, Thore Graepel, and Daniel Tarlow, Probabilistic Programs as Spreadsheet Queries, no. MSR-TR-2014-135, November 2014.

Vittal Premachandran, Daniel Tarlow, and Dhruv Batra, Empirical Minimum Bayes Risk Prediction: How to extract an extra few% performance from vision models with just three more parameters, in Proceedings of Computer Vision and Pattern Recognition (CVPR), June 2014.

Chris J Maddison and Daniel Tarlow, Structured Generative Models of Natural Source Code, in The 31st International Conference on Machine Learning (ICML), June 2014.

Nir Rosenfeld, Ofer Meshi, Daniel Tarlow, and Amir Globerson, Learning Structured Models with the AUC Loss and Its Generalizations, in Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS), April 2014.

Daniel Tarlow, Thore Graepel, and Tom Minka, Knowing what we don't know in NCAA Football ratings: Understanding and using structured uncertainty, in MIT Sloan Sports Analytics Conference, MIT Press, February 2014.

2013

Daniel Tarlow, Kevin Swersky, Laurent Charlin, Ilya Sutskever, and Richard S Zemel, Stochastic k-Neighborhood Selection for Supervised and Unsupervised Learning, in Proceedings of the International Conference on Machine Learning (ICML), 2013.

Yujia Li, Daniel Tarlow, and Richard Zemel, Exploring Compositional High Order Pattern Potentials for Structured Output Learning, in Proc. of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013)., 2013.

Robert Nishihara, Thomas Minka, and Daniel Tarlow, Detecting Parameter Symmetries in Probabilistic Models, in arXiv preprint arXiv:1312.5386, 2013.

Elad Mezuman*, Daniel Tarlow*, Amir Globerson, and Yair Weiss, Tighter Linear Program Relaxations for High Order Graphical Models, in Proc. of the 29th Conference on Uncertainty in Artificial Intelligence (UAI), 2013.

Nicolas Heess, Daniel Tarlow, and John Winn, Learning to Pass Expectation Propagation Messages, in Advances in Neural Information Processing Systems, 2013.

2012

Kevin Swersky, Daniel Tarlow, Ryan Adams, Rich Zemel, and Brendan Frey, Probabilistic n-Choose-k Models for Classification and Ranking, in Advances in Neural Information Processing Systems 25, 2012.

Daniel Tarlow and Richard S Zemel, Structured Output Learning with High Order Loss Functions, in Proceedings of the 15th Conference on Artificial Intelligence and Statistics, 2012.

Kevin Swersky, Daniel Tarlow, Ilya Sutskever, Ruslan Salakhutdinov, Rich Zemel, and Ryan Adams, Cardinality Restricted Boltzmann Machines, in Advances in Neural Information Processing Systems 25, 2012.

Daniel Tarlow and Ryan Adams, Revisiting Uncertainty in Graph Cut Solutions, in Proceedings of the 25th IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2012.

Daniel Tarlow, Ryan Prescott Adams, and Richard S Zemel, Randomized optimum models for structured prediction, in Proceedings of the 15th Conference on Artificial Intelligence and Statistics, 2012.

Daniel Tarlow, Kevin Swersky, Richard S Zemel, Ryan P Adams, and Brendan J Frey, Fast Exact Inference for Recursive Cardinality Models, in Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, 2012.

2011

Daniel Tarlow and Richard Zemel, Large Margin Learning with High Order Loss Functions, in Workshop on Inference in Graphical Models with Structured Potentials at Computer Vision and Pattern Recognition (CVPR), 2011.

Daniel Tarlow, Dhruv Batra, Pushmeet Kohli, and Vladimir Kolmogorov, Dynamic tree block coordinate ascent, in Proceedings of the International Conference on Machine Learning (ICML), 2011.

Daniel Tarlow, Inmar Givoni, Richard Zemel, and Brendan Frey, Graph Cuts is a Max-Product Algorithm, in Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), 2011.

2010

Daniel Tarlow, Inmar E Givoni, and Richard S Zemel, HOP-MAP: Efficient message passing with high order potentials, in Proceedings of 13th Conference on Artificial Intelligence and Statistics, 2010.

David A Ross, Daniel Tarlow, and Richard S Zemel, Learning articulated structure and motion, in International journal of computer vision, vol. 88, no. 2, pp. 214–237, Springer, 2010.

2009

Daniel Tarlow, Andrew Peterman, Benedict Schwegler, and Christopher Trigg, Automatically Calibrating a Probabilistic Graphical Model of Building Energy Consumption, in The 11th International Building Performance Simulation Association Conference on Building Simulation, 2009.

2008

Daniel Tarlow, Richard Zemel, and Brendan Frey, Flexible Priors for Exemplar-based Clustering, in Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI), 2008.

David Ross, Daniel Tarlow, and Richard Zemel, Unsupervised learning of skeletons from motion, in European Conference on Computer Vision, 2008.

2007

John Duchi, Daniel Tarlow, Gal Elidan, and Daphne Koller, Using Combinatorial Optimization within Max-Product Belief Propagation, in Advances in Neural Information Processing Systems (NIPS), 2007.

David A Ross, Daniel Tarlow, and Richard S Zemel, Learning articulated skeletons from motion, in Proceedings of Workshop on Dynamical Vision at ICCV, Citeseer, 2007.