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All publications by Amir Globerson
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How Hard is Inference for Structured Prediction?
Amir Globerson, Tim Roughgarden, David Sontag and Cafer Yildirim
Proceedings of the 32nd International Conference on Machine Learning (ICML-15), 2015


Spectral Regularization for Max-Margin Sequence Tagging
Ariadna Quattoni, Borja Balle, Xavier Carreras and Amir Globerson
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Inferning with High Girth Graphical Models
Uri Heinemann and Amir Globerson
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


Discrete Chebyshev Classifiers
Elad Eban, Elad Mezuman and Amir Globerson
Proceedings of the 31st International Conference on Machine Learning (ICML-14), 2014


The Pairwise Piecewise-Linear Embedding for Efficient Non-Linear Classification
Ofir Pele, Ben Taskar, Amir Globerson and Michael Werman
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Vanishing Component Analysis
Roi Livni, David Lehavi, Sagi Schein, Hila Nachliely, Shai Shalev-shwartz and Amir Globerson
Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013


Convergence Rate Analysis of MAP Coordinate Minimization Algorithms
Ofer Meshi, Amir Globerson and Tommi S. Jaakkola
Advances in Neural Information Processing Systems 25, 2012


Learning the Experts for Online Sequence Prediction
Elad Eban, Aharon Birnbaum, Shai Shalev-shwartz and Amir Globerson
Proceedings of the 29th International Conference on Machine Learning (ICML-12), 2012


A Simple Geometric Interpretation of SVM using Stochastic Adversaries
Roi Livni, Koby Crammer and Amir Globerson
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Learning Efficiently with Approximate Inference via Dual Losses
Ofer Meshi, David Sontag, Amir Globerson and Tommi S. Jaakkola
Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010


More data means less inference: A pseudo-max approach to structured learning
David Sontag, Ofer Meshi, Amir Globerson and Tommi S. Jaakkola
Advances in Neural Information Processing Systems 23, 2010


Learning Bayesian Network Structure using LP Relaxations
Tommi Jaakkola, David Sontag, Amir Globerson and Marina Meila
Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics (AISTATS-10), 2010


An LP View of the M-best MAP problem
Menachem Fromer and Amir Globerson
Advances in Neural Information Processing Systems 22, 2009


Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras and Peter L. Bartlett
Journal of Machine Learning Research, 2008


Clusters and Coarse Partitions in LP Relaxations
David Sontag, Amir Globerson and Tommi S. Jaakkola
Advances in Neural Information Processing Systems 21, 2008


Exponentiated gradient algorithms for log-linear structured prediction
Amir Globerson, Terry Koo, Xavier Carreras and Michael Collins
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations
Amir Globerson and Tommi S. Jaakkola
Advances in Neural Information Processing Systems 20, 2007


Convex Learning with Invariances
Choon H. Teo, Amir Globerson, Sam T. Roweis and Alex J. Smola
Advances in Neural Information Processing Systems 20, 2007


Euclidean Embedding of Co-occurrence Data
Amir Globerson, Gal Chechik, Fernando Pereira and Naftali Tishby
Journal of Machine Learning Research, 2007


Visualizing pairwise similarity via semidefinite programming
Amir Globerson and Sam T. Roweis
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Approximate inference using conditional entropy decompositions
Amir Globerson and Tommi Jaakkola
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


Nightmare at test time: robust learning by feature deletion
Amir Globerson and Sam T. Roweis
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Approximate inference using planar graph decomposition
Amir Globerson and Tommi S. Jaakkola
Advances in Neural Information Processing Systems 19, 2006


Metric Learning by Collapsing Classes
Amir Globerson and Sam T. Roweis
Advances in Neural Information Processing Systems 18, 2005


Information Bottleneck for Gaussian Variables
Gal Chechik, Amir Globerson, Naftali Tishby and Yair Weiss
Journal of Machine Learning Research, 2005


Euclidean Embedding of Co-Occurrence Data
Amir Globerson, Gal Chechik, Fernando Pereira and Naftali Tishby
Advances in Neural Information Processing Systems 17, 2004


Information Bottleneck for Gaussian Variables
Gal Chechik, Amir Globerson, Naftali Tishby and Yair Weiss
Advances in Neural Information Processing Systems 16, 2003


Sufficient Dimensionality Reduction
Amir Globerson and Naftali Tishby
Journal of Machine Learning Research, 2003


Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway
Gal Chechik, Amir Globerson, M. J. Anderson, E. D. Young, Israel Nelken and Naftali Tishby
Advances in Neural Information Processing Systems 14, 2001