Amir Globerson

Conference Proceedings

• An LP view of the M best problem

Menachem Fromer and Amir Globerson
Advances in Neural Information Processing Systems (NIPS) 22, 2009. Received the Outstanding Student Paper Award.

• Convexifying the Bethe Free Energy

Ofer Meshi, Ariel Jaimovich, Amir Globerson and Nir Friedman
Proceedings of Uncertainty in Artificial Intelligence (UAI). Montreal, Canada. 2009.

• Convergent message passing algorithms - a unifying view

Talya Meltzer, Amir Globerson and Yair Weiss
Proceedings of Uncertainty in Artificial Intelligence (UAI). Montreal, Canada. 2009.

• Clusters and Coarse Partitions in LP Relaxations

David Sontag, Amir Globerson and Tommi Jaakkola
Advances in Neural Information Processing Systems (NIPS) 21. Vancouver, Canada. 2008.

• Tightening LP Relaxations for MAP using Message Passing

David Sontag, Talya Meltzer. Amir Globerson, Tommi Jaakkola and Yair Weiss
Uncertainty in Artificial Intelligence (UAI). Helsinki, Finland. 2008.
Received the best paper award.

• Fixing max-product: Convergent message passing algorithms for MAP LP-relaxations

Amir Globerson , Tommi Jaakkola
Advances in Neural Information Processing Systems (NIPS) 20. Vancouver, Canada. 2007.

• Convex Learning with Invariances

Choon Hui Teo, Amir Globerson , Sam Roweis and Alex Smola
Advances in Neural Information Processing Systems (NIPS) 20. Vancouver, Canada. 2007.

• Convergent Propagation Algorithms via Oriented Trees

Amir Globerson and Tommi Jaakkola
Uncertainty in Artificial Intelligence (UAI), 2007.
Received the Best Paper Award

• Structured Prediction Models via The Matrix-Tree Theorem

Terry Koo, Amir Globerson, Xavier Carreras and Michael Collins
Empirical Methods in Natural Language Processing (EMNLP), 2007.

• Exponentiated Gradient Algorithms for Log-Linear Structured Prediction

Amir Globerson, Terry Koo, Xavier Carreras and Michael Collins
Proceedings of the Interational Conference on Machine Learning (ICML) , 2007.

• Approximate Inference Using Conditional Entropy Decompositions

Amir Globerson and Tommi Jaakkola
The 11th International Workshop on Artificial Intelligence and Statistics (AISTATS). Puerto-Rico 2007.

• Visualizing Pairwise Similarity via Semidefinite Programming

Amir Globerson and Sam Roweis
The 11th International Workshop on Artificial Intelligence and Statistics (AISTATS). Puerto-Rico 2007.

• Approximate Inference Using Planar Graph Decomposition

Amir Globerson , Tommi Jaakkola
Advances in Neural Information Processing Systems (NIPS) 19. Vancouver, Canada. 2006.

• Discriminative Learning via Semidefinite Probabilities

Koby Crammer, Amir Globerson
Proceedings of Uncertainty in Artificial Intelligence (UAI) , Boston, MA. 2006.

• Nightmare at Test Time: Robust Learning by Feature Deletion

Amir Globerson, Sam Roweis
Proceedings of the Interational Conference on Machine Learning (ICML) , Pittsburgh, PA. 2006.

• Metric Learning by Collapsing Classes

Amir Globerson, Sam Roweis
Advances in Neural Information Processing Systems (NIPS) 18. Vancouver, Canada. 2005.

• Euclidean Embedding of Co-occurrence data

Amir Globerson, Gal Chechik, Fernando Pereira and Naftali Tishby
Advances in Neural Information Processing Systems (NIPS) 17. Vancouver, Canada. 2004.
Received the Outstanding Student Paper Award.

• Distributed Latent Variable Models of Lexical Co-occurrences

John Blitzer, Amir Globerson and Fernando Pereira
Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS). Barbados 2005.

• The Minimum Information Principle in Discriminative Learning

Amir Globerson and Naftali Tishby
Uncertainty in Artificial Inteligence (UAI). Banff ,Canada 2004.
 
• Gaussian Information Bottleneck

Gal Chechik, Amir Globerson, Naftali Tishby and Yair Weiss
Advances in Neural Information Processing Systems (NIPS) 17.Vancouver, Canada.  2003.

• Sufficient Dimensionality Reduction with Irrelevance Statistics.

Amir Globerson, Gal Chechik, and Naftali Tishby
Uncertainty in Artificial Inteligence (UAI). Acapulco, Mexico. 2003.

• Sufficient Dimensionality Reduction - A Novel Analysis Principle

Amir Globerson and Naftali Tishby
International Conference on Machine Learning (ICML). Sydney, Australia. 2002.

• Groups Redundancy Measures Reveal Redundancy Reduction Along the Auditory Pathway

Gal Chechik, Amir Globerson, Michael Anderson, Eric Young, Israel Nelken and N. Tishby
Advances in Neural Information Processing Systems (NIPS) 14. Vancouver  Canada,  2001.

PhD Thesis

• The Minimum Information Principle in Learning and Neural Data Analysis

The Hebrew University, Jerusalem, 2005. Advisors: Prof. N. Tishby and Prof. E. Vaadia

Technical Reports and Abstracts

• On the Optimality of the Gaussian Information Bottleneck Curve

Amir Globerson and Naftali Tishby
Hebrew University Technical Report, 2004

• Distributional Clustering of Movements Based on Neural Responses

Amir Globerson, Gal Chechik, Orna Steinberg, Eilon Vaadia and Natali Tishby.
Hebrew University Technical Report, 2004

• Information Bounds on Vectors with Applications to Nonstationary and Population Coding

Amir Globerson, Eran Stark, Ron Paz, Eilon Vaadia and Naftali Tishby.
Abstracts of papers presetned at the CSHL 2004 Meeting on Computational and Systems Neuroscience (COSYNE)

Teaching

• 2008: Probabilstic methods in artificial intelligence
• 2003-2005 : Teaching the course Computation, Information, and Learning I
• 2000-2003 : TA in Introduction to linear systems