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 | 2012 | |
|---|---|---|
| j8 |      | Roi Livni, Koby Crammer, Amir Globerson: A Simple Geometric Interpretation of SVM using Stochastic Adversaries. Journal of Machine Learning Research - Proceedings Track 22: 722-730 (2012) |
| c31 | | Tahira Naseem, Regina Barzilay, Amir Globerson: Selective Sharing for Multilingual Dependency Parsing. ACL (1) 2012: 629-637 |
| c30 | | Yuan Zhang, Roi Reichart, Regina Barzilay, Amir Globerson: Learning to Map into a Universal POS Tagset. EMNLP-CoNLL 2012: 1368-1378 |
| c29 | | Alexander M. Rush, Roi Reichart, Michael Collins, Amir Globerson: Improved Parsing and POS Tagging Using Inter-Sentence Consistency Constraints. EMNLP-CoNLL 2012: 1434-1444 |
| c28 | | Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson: Learning the Experts for Online Sequence Prediction. ICML 2012 |
| c27 | | Ofer Meshi, Tommi Jaakkola, Amir Globerson: Convergence Rate Analysis of MAP Coordinate Minimization Algorithms. NIPS 2012: 3023-3031 |
| i9 | | Uri Heinemann, Amir Globerson: What Cannot be Learned with Bethe Approximations. CoRR abs/1202.3731 (2012) |
| i8 | | Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman: Convexifying the Bethe Free Energy. CoRR abs/1205.2624 (2012) |
| i7 | | Talya Meltzer, Amir Globerson, Yair Weiss: Convergent message passing algorithms - a unifying view. CoRR abs/1205.2625 (2012) |
| i6 | | David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing. CoRR abs/1206.3288 (2012) |
| i5 | | Elad Eban, Aharon Birnbaum, Shai Shalev-Shwartz, Amir Globerson: Learning the Experts for Online Sequence Prediction. CoRR abs/1206.4604 (2012) |
| i4 | | Amir Globerson, Tommi Jaakkola: Convergent Propagation Algorithms via Oriented Trees. CoRR abs/1206.5243 (2012) |
| i3 | | Amir Globerson, Naftali Tishby: The Minimum Information Principle for Discriminative Learning. CoRR abs/1207.4110 (2012) |
| i2 | | Amir Globerson, Gal Chechik, Naftali Tishby: Sufficient Dimensionality Reduction with Irrelevant Statistics. CoRR abs/1212.2483 (2012) |
| 2011 | ||
| c26 | | Ofer Meshi, Amir Globerson: An Alternating Direction Method for Dual MAP LP Relaxation. ECML/PKDD (2) 2011: 470-483 |
| c25 | | |
| i1 | | |
| 2010 | ||
| j7 | | Tommi Jaakkola, David Sontag, Amir Globerson, Marina Meila: Learning Bayesian Network Structure using LP Relaxations. Journal of Machine Learning Research - Proceedings Track 9: 358-365 (2010) |
| c24 | | Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson: Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790 |
| c23 | | David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson: More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189 |
| 2009 | ||
| c22 | | |
| c21 | | Talya Meltzer, Amir Globerson, Yair Weiss: Convergent message passing algorithms - a unifying view. UAI 2009: 393-401 |
| c20 | | Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman: Convexifying the Bethe Free Energy. UAI 2009: 402-410 |
| 2008 | ||
| j6 | | Michael Collins, Amir Globerson, Terry Koo, Xavier Carreras, Peter L. Bartlett: Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks. Journal of Machine Learning Research 9: 1775-1822 (2008) |
| c19 | | David Sontag, Amir Globerson, Tommi Jaakkola: Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544 |
| c18 | | David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510 |
| 2007 | ||
| j5 | | Amir Globerson, Tommi Jaakkola: Approximate inference using conditional entropy decompositions. Journal of Machine Learning Research - Proceedings Track 2: 130-138 (2007) |
| j4 | | Amir Globerson, Sam T. Roweis: Visualizing pairwise similarity via semidefinite programming. Journal of Machine Learning Research - Proceedings Track 2: 139-146 (2007) |
| j3 | | Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby: Euclidean Embedding of Co-occurrence Data. Journal of Machine Learning Research 8: 2265-2295 (2007) |
| c17 | | Terry Koo, Amir Globerson, Xavier Carreras, Michael Collins: Structured Prediction Models via the Matrix-Tree Theorem. EMNLP-CoNLL 2007: 141-150 |
| c16 | | Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins: Exponentiated gradient algorithms for log-linear structured prediction. ICML 2007: 305-312 |
| c15 | | Amir Globerson, Tommi Jaakkola: Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS 2007 |
| c14 | | Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola: Convex Learning with Invariances. NIPS 2007 |
| c13 | | Amir Globerson, Tommi Jaakkola: Convergent Propagation Algorithms via Oriented Trees. UAI 2007: 133-140 |
| 2006 | ||
| c12 | | Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby: Embedding Heterogeneous Data Using Statistical Models. AAAI 2006: 1605-1608 |
| c11 | | Amir Globerson, Sam T. Roweis: Nightmare at test time: robust learning by feature deletion. ICML 2006: 353-360 |
| c10 | | Amir Globerson, Tommi Jaakkola: Approximate inference using planar graph decomposition. NIPS 2006: 473-480 |
| c9 | | Koby Crammer, Amir Globerson: Discriminative Learning via Semidefinite Probabilistic Models. UAI 2006 |
| 2005 | ||
| j2 | | Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss: Information Bottleneck for Gaussian Variables. Journal of Machine Learning Research 6: 165-188 (2005) |
| c8 | | |
| 2004 | ||
| c7 | | Amir Globerson, Gal Chechik, Fernando C. Pereira, Naftali Tishby: Euclidean Embedding of Co-Occurrence Data. NIPS 2004 |
| c6 | | Amir Globerson, Naftali Tishby: The Minimum Information Principle for Discriminative Learning. UAI 2004: 193-200 |
| 2003 | ||
| j1 | | Amir Globerson, Naftali Tishby: Sufficient Dimensionality Reduction. Journal of Machine Learning Research 3: 1307-1331 (2003) |
| c5 | | Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss: Information Bottleneck for Gaussian Variables. NIPS 2003 |
| c4 | | Amir Globerson, Gal Chechik, Naftali Tishby: Sufficient Dimensionality Reduction with Irrelevance Statistics. UAI 2003: 281-288 |
| 2002 | ||
| c3 | | |
| c2 |  | Amir Globerson, Naftali Tishby: Sufficient Dimensionality Reduction - A novel Analysis Method. ICML 2002: 203-210 |
| 2001 | ||
| c1 | | Gal Chechik, Amir Globerson, M. J. Anderson, E. D. Young, Israel Nelken, Naftali Tishby: Group Redundancy Measures Reveal Redundancy Reduction in the Auditory Pathway. NIPS 2001: 173-180 |
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