Koby Crammer

	2012
63		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
62		Nina Vaits, Koby Crammer: Re-adapting the Regularization of Weights for Non-stationary Regression. ALT 2011: 114-128
61		Koby Crammer, Claudio Gentile: Multiclass Classification with Bandit Feedback using Adaptive Regularization. ICML 2011: 273-280
60		Noam Slonim, Elad Yom-Tov, Koby Crammer: Active Online Classification via Information Maximization. IJCAI 2011: 1498-1504
59		Zhuang Wang, Nemanja Djuric, Koby Crammer, Slobodan Vucetic: Trading representability for scalability: adaptive multi-hyperplane machine for nonlinear classification. KDD 2011: 24-32
58		Avihai Mejer, Koby Crammer: Confidence Estimation in Structured Prediction CoRR abs/1111.1386: (2011)
	2010
57		Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer: Learning Better Data Representation Using Inference-Driven Metric Learning. ACL (Short Papers) 2010: 377-381
56		Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jennifer Wortman Vaughan: Regret Minimization With Concept Drift. COLT 2010: 168-180
55		Avihai Mejer, Koby Crammer: Confidence in Structured-Prediction Using Confidence-Weighted Models. EMNLP 2010: 971-981
54		Koby Crammer: Efficient online learning with individual learning-rates for phoneme sequence recognition. ICASSP 2010: 4878-4881
53		Zhuang Wang, Koby Crammer, Slobodan Vucetic: Multi-Class Pegasos on a Budget. ICML 2010: 1143-1150
52		Francesco Orabona, Koby Crammer: New Adaptive Algorithms for Online Classification. NIPS 2010: 1840-1848
51		Koby Crammer, Daniel Lee: Learning via Gaussian Herding. NIPS 2010: 451-459
50		Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira: Exploiting Feature Covariance in High-Dimensional Online Learning. Journal of Machine Learning Research - Proceedings Track 9: 493-500 (2010)
49		Mark Dredze, Alex Kulesza, Koby Crammer: Multi-domain learning by confidence-weighted parameter combination. Machine Learning 79(1-2): 123-149 (2010)
48		Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan: A theory of learning from different domains. Machine Learning 79(1-2): 151-175 (2010)
	2009
47		Partha Pratim Talukdar, Koby Crammer: New Regularized Algorithms for Transductive Learning. ECML/PKDD (2) 2009: 442-457
46		Koby Crammer, Mark Dredze, Alex Kulesza: Multi-Class Confidence Weighted Algorithms. EMNLP 2009: 496-504
45		Kedar Bellare, Koby Crammer, Dayne Freitag: Loss-Sensitive Discriminative Training of Machine Transliteration Models. HLT-NAACL (Student Research Workshop and Doctoral Consortium) 2009: 61-65
44		Ted Sandler, Lyle H. Ungar, Koby Crammer: Resolving Identity Uncertainty with Learned Random Walks. ICDM 2009: 457-465
43		Hui Lin, Jeff Bilmes, Koby Crammer: How to loose confidence: probabilistic linear machines for multiclass classification. INTERSPEECH 2009: 2559-2562
42		Koby Crammer, Alex Kulesza, Mark Dredze: Adaptive Regularization of Weight Vectors. NIPS 2009: 414-422
41		Koby Crammer, Mehryar Mohri, Fernando Pereira: Gaussian Margin Machines. Journal of Machine Learning Research - Proceedings Track 5: 105-112 (2009)
	2008
40		Mark Dredze, Koby Crammer: Active Learning with Confidence. ACL (Short Papers) 2008: 233-236
39		Koby Crammer: Advanced Online Learning for Natural Language Processing. ACL (Tutorial Abstracts) 2008: 4
38		Ron Bekkerman, Koby Crammer: One-Class Clustering in the Text Domain. EMNLP 2008: 41-50
37		Mark Dredze, Koby Crammer: Online Methods for Multi-Domain Learning and Adaptation. EMNLP 2008: 689-697
36		Koby Crammer, Partha Pratim Talukdar, Fernando Pereira: A rate-distortion one-class model and its applications to clustering. ICML 2008: 184-191
35		Mark Dredze, Koby Crammer, Fernando Pereira: Confidence-weighted linear classification. ICML 2008: 264-271
34		Koby Crammer, Mark Dredze, Fernando Pereira: Exact Convex Confidence-Weighted Learning. NIPS 2008: 345-352
33		Qian Liu, Koby Crammer, Fernando C. N. Pereira, David S. Roos: Reranking candidate gene models with cross-species comparison for improved gene prediction. BMC Bioinformatics 9: (2008)
32		Koby Crammer, Michael Kearns, Jennifer Wortman: Learning from Multiple Sources. Journal of Machine Learning Research 9: 1757-1774 (2008)
31		Partha Pratim Talukdar, Marie Jacob, Muhammad Salman Mehmood, Koby Crammer, Zachary G. Ives, Fernando Pereira, Sudipto Guha: Learning to create data-integrating queries. PVLDB 1(1): 785-796 (2008)
	2007
30		Koby Crammer: A conservative aggressive subspace tracker. INTERSPEECH 2007: 498-501
29		John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman: Learning Bounds for Domain Adaptation. NIPS 2007
28		Axel Bernal, Koby Crammer, Artemis G. Hatzigeorgiou, Fernando Pereira: Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction. PLoS Computational Biology 3(3): (2007)
	2006
27		Linli Xu, Koby Crammer, Dale Schuurmans: Robust Support Vector Machine Training via Convex Outlier Ablation. AAAI 2006: 536-542
26		Koby Crammer: Online Tracking of Linear Subspaces. COLT 2006: 438-452
25		Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira: Analysis of Representations for Domain Adaptation. NIPS 2006: 137-144
24		Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Multiple Sources. NIPS 2006: 321-328
23		Koby Crammer, Amir Globerson: Discriminative Learning via Semidefinite Probabilistic Models. UAI 2006
22		Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer: Online Passive-Aggressive Algorithms. Journal of Machine Learning Research 7: 551-585 (2006)
	2005
21		Ryan T. McDonald, Koby Crammer, Fernando C. N. Pereira: Online Large-Margin Training of Dependency Parsers. ACL 2005
20		Koby Crammer, Yoram Singer: Loss Bounds for Online Category Ranking. COLT 2005: 48-62
19		Ryan T. McDonald, Koby Crammer, Fernando Pereira: Flexible Text Segmentation with Structured Multilabel Classification. HLT/EMNLP 2005
18		Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Data of Variable Quality. NIPS 2005
17		Koby Crammer, Yoram Singer: Online Ranking by Projecting. Neural Computation 17(1): 145-175 (2005)
	2004
16		Koby Crammer, Gal Chechik: A needle in a haystack: local one-class optimization. ICML 2004
15		Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer: A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities. NIPS 2004
	2003
14		Koby Crammer, Yoram Singer: Learning Algorithm for Enclosing Points in Bregmanian Spheres. COLT 2003: 388-402
13		Koby Crammer, Jaz S. Kandola, Yoram Singer: Online Classification on a Budget. NIPS 2003
12		Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer: Online Passive-Aggressive Algorithms. NIPS 2003
11		Koby Crammer, Yoram Singer: A Family of Additive Online Algorithms for Category Ranking. Journal of Machine Learning Research 3: 1025-1058 (2003)
10		Koby Crammer, Yoram Singer: Ultraconservative Online Algorithms for Multiclass Problems. Journal of Machine Learning Research 3: 951-991 (2003)
	2002
9		Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby: Margin Analysis of the LVQ Algorithm. NIPS 2002: 462-469
8		Koby Crammer, Joseph Keshet, Yoram Singer: Kernel Design Using Boosting. NIPS 2002: 537-544
7		Koby Crammer, Yoram Singer: A new family of online algorithms for category ranking. SIGIR 2002: 151-158
6		Koby Crammer, Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. Machine Learning 47(2-3): 201-233 (2002)
	2001
5		Koby Crammer, Yoram Singer: Ultraconservative Online Algorithms for Multiclass Problems. COLT/EuroCOLT 2001: 99-115
4		Koby Crammer, Yoram Singer: Pranking with Ranking. NIPS 2001: 641-647
3		Koby Crammer, Yoram Singer: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. Journal of Machine Learning Research 2: 265-292 (2001)
	2000
2		Koby Crammer, Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000: 35-46
1		Koby Crammer, Yoram Singer: Improved Output Coding for Classification Using Continuous Relaxation. NIPS 2000: 437-443

Coauthor Index

1	Ron Bekkerman	[38]
2	Kedar Bellare	[45]
3	Shai Ben-David	[25] [48]
4	Axel Bernal	[28]
5	Jeff A. Bilmes (Jeff Bilmes)	[43]
6	John Blitzer (John C. Blitzer)	[25] [29] [48]
7	Gal Chechik	[16]
8	Ofer Dekel	[12] [22]
9	Paramveer S. Dhillon	[57]
10	Nemanja Djuric	[59]
11	Mark Dredze	[34] [35] [37] [40] [42] [46] [49] [50]
12	Eyal Even-Dar	[56]
13	Dayne Freitag	[45]
14	Claudio Gentile	[61]
15	Ran Gilad-Bachrach (Ran Bachrach)	[9]
16	Amir Globerson	[23] [63]
17	Sudipto Guha	[31]
18	Artemis G. Hatzigeorgiou	[28]
19	Zachary G. Ives	[31]
20	Marie Jacob	[31]
21	Jaz S. Kandola	[13]
22	Michael Kearns (Michael J. Kearns, Michael S. Kearns)	[18] [24] [32]
23	Joseph Keshet	[8] [22]
24	Alex Kulesza	[29] [42] [46] [48] [49] [50]
25	Daniel Lee	[51]
26	Hui Lin	[43]
27	Qian Liu	[33]
28	Roi Livni	[63]
29	Justin Ma	[50]
30	Yishay Mansour	[56]
31	Ryan T. McDonald	[19] [21]
32	Muhammad Salman Mehmood	[31]
33	Avihai Mejer	[55] [58]
34	Mehryar Mohri	[41]
35	Amir Navot	[9]
36	Francesco Orabona	[52]
37	Rony Paz	[15]
38	Fernando Pereira (Fernando Manuel Bernardo Pereira)	[19] [25] [28] [29] [31] [34] [35] [36] [41] [48] [50]
39	Fernando C. N. Pereira	[21] [33]
40	David S. Roos	[33]
41	Ted Sandler	[44]
42	Lawrence K. Saul	[50]
43	Dale Schuurmans	[27]
44	Shai Shalev-Shwartz	[12] [22]
45	Lavi Shpigelman	[15]
46	Yoram Singer	[1] [2] [3] [4] [5] [6] [7] [8] [10] [11] [12] [13] [14] [15] [17] [20] [22]
47	Noam Slonim	[60]
48	Partha Pratim Talukdar	[31] [36] [47] [57]
49	Naftali Tishby	[9]
50	Lyle H. Ungar	[44]
51	Eilon Vaadia	[15]
52	Nina Vaits	[62]
53	Jennifer Wortman Vaughan (Jennifer Wortman)	[18] [24] [29] [32] [48] [56]
54	Slobodan Vucetic	[53] [59]
55	Zhuang Wang	[53] [59]
56	Linli Xu	[27]
57	Elad Yom-Tov	[60]

Colors in the list of coauthors

Last update Tue May 29 01:28:40 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page