Ofer Meshi's Home Page

I have moved.
I am now a Research Scientist at Google. Previously I was a Research Assistant Professor at the Toyota Technological Institute at Chicago, a philanthropically endowed academic computer science institute located on the University of Chicago campus.
I obtained my Ph.D. and M.Sc. in Computer Science from the Hebrew University of Jerusalem, where I worked with Amir Globerson and Nir Friedman. My B.Sc. in Computer Science is from Tel Aviv University.
My research is in Machine learning and optimization. In particular, I am interested in finding efficient algorithms for: structured output prediction, probabilistic graphical models, model selection, statistical relational models and other related problems.

Contact Information

Email:

Publications

Newer publications can be found here

Linear-Memory and Decomposition-Invariant Linearly Convergent Conditional Gradient Algorithm for Structured Polytopes
D. Garber, O. Meshi

In Neural Information Processing Systems (NIPS) 2016.
Train and Test Tightness of LP Relaxations in Structured Prediction
O. Meshi, M. Mahdavi, A. Weller, D. Sontag

International Conference on Machine Learning (ICML) 2016.
Fast and Scalable Structural SVM with Slack Rescaling
H. Choi, O. Meshi, N. Srebro

Artificial Intelligence and Statistics (AISTATS) 2016.
On the Tightness of LP Relaxations for Structured Prediction
O. Meshi, M. Mahdavi, D. Sontag

NIPS Workshop on Optimization for Machine Learning, 2015.
Smooth and Strong: MAP Inference with Linear Convergence
O. Meshi, M. Mahdavi, A. Schwing

Neural Information Processing Systems (NIPS) 2015.
Efficient Training of Structured SVMs via Soft Constraints
O. Meshi, N. Srebro, T. Hazan

Artificial Intelligence and Statistics (AISTATS) 2015.

Smoothed Coordinate Descent for MAP Inference
O. Meshi, T. Jaakkola, A. Globerson

Advanced Structured Prediction, editors S. Nowozin, P. V. Gehler, J. Jancsary, C. Lampert, MIT Press 2014.

Learning Structured Models with the AUC Loss and Its Generalizations (supplementary)
N. Rosenfeld, O. Meshi, D. Tarlow, A. Globerson

Artificial Intelligence and Statistics (AISTATS) 2014.

Learning Max-Margin Tree Predictors
O. Meshi, E. Eban, G. Elidan, A. Globerson

Uncertainty in Artificial Intelligence (UAI) 2013.

Convergence Rate Analysis of MAP Coordinate Minimization Algorithms (supplementary)
O. Meshi, T. Jaakkola, A. Globerson

Neural Information Processing Systems (NIPS) 2012.

An Alternating Direction Method for Dual MAP LP Relaxation
O. Meshi and A. Globerson

European Conference on Machine Learning (ECML PKDD) 2011.

More data means less inference: A pseudo-max approach to structured learning (supplementary)
D. Sontag, O. Meshi, T. Jaakkola, A. Globerson

Neural Information Processing Systems (NIPS) 2010.

Learning Efficiently with Approximate Inference via Dual Losses
O. Meshi, D. Sontag, T. Jaakkola and A. Globerson

International Conference on Machine Learning (ICML) 2010.

FastInf: An Efficient Approximate Inference Library
A. Jaimovich, O. Meshi, I. McGraw, G. Elidan

Journal of Machine Learning Research (JMLR), 11:1733-1736, 2010.

Convexifying the Bethe Free Energy
O. Meshi, A. Jaimovich, A. Globerson and N. Friedman

Uncertainty in Artificial Intelligence (UAI) 2009.

Template Based Inference in Symmetric Relational Markov Random Fields
A. Jaimovich, O. Meshi and N. Friedman

Uncertainty in Artificial Intelligence (UAI) 2007.

Evolutionary Conservation and over-representation of functionally enriched network patterns in the yeast regulatory network
O. Meshi, T. Shlomi and E. Ruppin

BMC Systems Biology, 1:1, 2007.

Theses

Learning Symmetric Relational Markov Random Fields
O. Meshi, MSc dissertation, 2007.
Efficient Methods for Learning and Inference in Structured Output Prediction
O. Meshi, PhD dissertation, 2013.

Personal

Avital Meshi