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
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Publications
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
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