Amir Globerson

	2012
36		Uri Heinemann, Amir Globerson: What Cannot be Learned with Bethe Approximations CoRR abs/1202.3731: (2012)
35		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)
	2011
34		Ofer Meshi, Amir Globerson: An Alternating Direction Method for Dual MAP LP Relaxation. ECML/PKDD (2) 2011: 470-483
33		Uri Heinemann, Amir Globerson: What Cannot be Learned with Bethe Approximations. UAI 2011: 319-326
32		Ido Ginodi, Amir Globerson: Gaussian Robust Classification CoRR abs/1104.0235: (2011)
	2010
31		Ofer Meshi, David Sontag, Tommi Jaakkola, Amir Globerson: Learning Efficiently with Approximate Inference via Dual Losses. ICML 2010: 783-790
30		David Sontag, Ofer Meshi, Tommi Jaakkola, Amir Globerson: More data means less inference: A pseudo-max approach to structured learning. NIPS 2010: 2181-2189
29		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)
	2009
28		Menachem Fromer, Amir Globerson: An LP View of the M-best MAP problem. NIPS 2009: 567-575
27		Talya Meltzer, Amir Globerson, Yair Weiss: Convergent message passing algorithms - a unifying view. UAI 2009: 393-401
26		Ofer Meshi, Ariel Jaimovich, Amir Globerson, Nir Friedman: Convexifying the Bethe Free Energy. UAI 2009: 402-410
	2008
25		David Sontag, Amir Globerson, Tommi Jaakkola: Clusters and Coarse Partitions in LP Relaxations. NIPS 2008: 1537-1544
24		David Sontag, Talya Meltzer, Amir Globerson, Tommi Jaakkola, Yair Weiss: Tightening LP Relaxations for MAP using Message Passing. UAI 2008: 503-510
23		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)
	2007
22		Terry Koo, Amir Globerson, Xavier Carreras, Michael Collins: Structured Prediction Models via the Matrix-Tree Theorem. EMNLP-CoNLL 2007: 141-150
21		Amir Globerson, Terry Koo, Xavier Carreras, Michael Collins: Exponentiated gradient algorithms for log-linear structured prediction. ICML 2007: 305-312
20		Choon Hui Teo, Amir Globerson, Sam T. Roweis, Alex J. Smola: Convex Learning with Invariances. NIPS 2007
19		Amir Globerson, Tommi Jaakkola: Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. NIPS 2007
18		Amir Globerson, Tommi Jaakkola: Convergent Propagation Algorithms via Oriented Trees. UAI 2007: 133-140
17		Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby: Euclidean Embedding of Co-occurrence Data. Journal of Machine Learning Research 8: 2265-2295 (2007)
16		Amir Globerson, Tommi Jaakkola: Approximate inference using conditional entropy decompositions. Journal of Machine Learning Research - Proceedings Track 2: 130-138 (2007)
15		Amir Globerson, Sam T. Roweis: Visualizing pairwise similarity via semidefinite programming. Journal of Machine Learning Research - Proceedings Track 2: 139-146 (2007)
	2006
14		Amir Globerson, Gal Chechik, Fernando Pereira, Naftali Tishby: Embedding Heterogeneous Data Using Statistical Models. AAAI 2006: 1605-1608
13		Amir Globerson, Sam T. Roweis: Nightmare at test time: robust learning by feature deletion. ICML 2006: 353-360
12		Amir Globerson, Tommi Jaakkola: Approximate inference using planar graph decomposition. NIPS 2006: 473-480
11		Koby Crammer, Amir Globerson: Discriminative Learning via Semidefinite Probabilistic Models. UAI 2006
	2005
10		Amir Globerson, Sam T. Roweis: Metric Learning by Collapsing Classes. NIPS 2005
9		Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss: Information Bottleneck for Gaussian Variables. Journal of Machine Learning Research 6: 165-188 (2005)
	2004
8		Amir Globerson, Gal Chechik, Fernando C. Pereira, Naftali Tishby: Euclidean Embedding of Co-Occurrence Data. NIPS 2004
7		Amir Globerson, Naftali Tishby: The Minimum Information Principle for Discriminative Learning. UAI 2004: 193-200
	2003
6		Gal Chechik, Amir Globerson, Naftali Tishby, Yair Weiss: Information Bottleneck for Gaussian Variables. NIPS 2003
5		Amir Globerson, Gal Chechik, Naftali Tishby: Sufficient Dimensionality Reduction with Irrelevance Statistics. UAI 2003: 281-288
4		Amir Globerson, Naftali Tishby: Sufficient Dimensionality Reduction. Journal of Machine Learning Research 3: 1307-1331 (2003)
	2002
3		Amir Globerson, Naftali Tishby: Most Informative Dimension Reduction. AAAI/IAAI 2002: 1024-1029
2		Amir Globerson, Naftali Tishby: Sufficient Dimensionality Reduction - A novel Analysis Method. ICML 2002: 203-210
	2001
1		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

Coauthor Index

1	M. J. Anderson	[1]
2	Peter L. Bartlett	[23]
3	Xavier Carreras	[21] [22] [23]
4	Gal Chechik	[1] [5] [6] [8] [9] [14] [17]
5	Michael Collins	[21] [22] [23]
6	Koby Crammer	[11] [35]
7	Nir Friedman	[26]
8	Menachem Fromer	[28]
9	Ido Ginodi	[32]
10	Uri Heinemann	[33] [36]
11	Tommi Jaakkola	[12] [16] [18] [19] [24] [25] [29] [30] [31]
12	Ariel Jaimovich	[26]
13	Terry Koo	[21] [22] [23]
14	Roi Livni	[35]
15	Marina Meila	[29]
16	Talya Meltzer	[24] [27]
17	Ofer Meshi	[26] [30] [31] [34]
18	Israel Nelken	[1]
19	Fernando Pereira (Fernando Manuel Bernardo Pereira)	[14] [17]
20	Fernando C. Pereira	[8]
21	Sam T. Roweis	[10] [13] [15] [20]
22	Alexander J. Smola (Alex J. Smola)	[20]
23	David Sontag	[24] [25] [29] [30] [31]
24	Choon Hui Teo	[20]
25	Naftali Tishby	[1] [2] [3] [4] [5] [6] [7] [8] [9] [14] [17]
26	Yair Weiss	[6] [9] [24] [27]
27	E. D. Young	[1]

Colors in the list of coauthors

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