Koby Crammer

Koby Crammer, Claudio Gentile: Multiclass classification with bandit feedback using adaptive regularization. Machine Learning 90(3): 347-383 (2013)

Koby Crammer, Alex Kulesza, Mark Dredze: Adaptive regularization of weight vectors. Machine Learning 91(2): 155-187 (2013)

Edward Moroshko, Koby Crammer: Weighted Last-Step Min-Max Algorithm with Improved Sub-Logarithmic Regret. CoRR abs/1301.6058 (2013)

Nina Vaits, Edward Moroshko, Koby Crammer: Second-Order Non-Stationary Online Learning for Regression. CoRR abs/1303.0140 (2013)

Edward Moroshko, Koby Crammer: A Last-Step Regression Algorithm for Non-Stationary Online Learning. CoRR abs/1303.3754 (2013)

Francesco Orabona, Koby Crammer, Nicolò Cesa-Bianchi: A Generalized Online Mirror Descent with Applications to Classification and Regression. CoRR abs/1304.2994 (2013)

Yevgeny Seldin, Peter L. Bartlett, Koby Crammer: Advice-Efficient Prediction with Expert Advice. CoRR abs/1304.3708 (2013)

Axel Bernal, Koby Crammer, Fernando Pereira: Automated gene-model curation using global discriminative learning. Bioinformatics 28(12): 1571-1578 (2012)

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)

Yoav Haimovitch, Koby Crammer, Shie Mannor: More Is Better: Large Scale Partially-supervised Sentiment Classication. Journal of Machine Learning Research - Proceedings Track 25: 175-190 (2012)

Edward Moroshko, Koby Crammer: Weighted Last-Step Min-Max Algorithm with Improved Sub-logarithmic Regret. ALT 2012: 245-259

Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer: Metric Learning for Graph-Based Domain Adaptation. COLING (Posters) 2012: 255-264

Koby Crammer, Daniel D. Lee: Online discriminative learning of phoneme recognition via collections of generalized linear models. ICASSP 2012: 1961-1964

Koby Crammer, Alex Kulesza, Mark Dredze: New ℌ^∞ bounds for the recursive least squares algorithm exploiting input structure. ICASSP 2012: 2017-2020

Koby Crammer, Gal Chechik: Adaptive Regularization for Similarity Measures. ICML 2012

Avihai Mejer, Koby Crammer: Training Dependency Parser Using Light Feedback. HLT-NAACL 2012: 488-497

Avihai Mejer, Koby Crammer: Are You Sure? Confidence in Prediction of Dependency Tree Edges. HLT-NAACL 2012: 573-576

Koby Crammer, Tal Wagner: Volume Regularization for Binary Classification. NIPS 2012: 341-349

Koby Crammer, Yishay Mansour: Learning Multiple Tasks using Shared Hypotheses. NIPS 2012: 1484-1492

Matan Orbach, Koby Crammer: Graph-Based Transduction with Confidence. ECML/PKDD (2) 2012: 323-338

Koby Crammer, Gal Chechik: Adaptive Regularization for Weight Matrices. CoRR abs/1206.4639 (2012)

Yoav Haimovitch, Koby Crammer, Shie Mannor: More Is Better: Large Scale Partially-supervised Sentiment Classification - Appendix. CoRR abs/1209.6329 (2012)

Nina Vaits, Koby Crammer: Re-adapting the Regularization of Weights for Non-stationary Regression. ALT 2011: 114-128

Koby Crammer, Claudio Gentile: Multiclass Classification with Bandit Feedback using Adaptive Regularization. ICML 2011: 273-280

Noam Slonim, Elad Yom-Tov, Koby Crammer: Active Online Classification via Information Maximization. IJCAI 2011: 1498-1504

Zhuang Wang, Nemanja Djuric, Koby Crammer, Slobodan Vucetic: Trading representability for scalability: adaptive multi-hyperplane machine for nonlinear classification. KDD 2011: 24-32

Avihai Mejer, Koby Crammer: Confidence Estimation in Structured Prediction. CoRR abs/1111.1386 (2011)

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)

Mark Dredze, Alex Kulesza, Koby Crammer: Multi-domain learning by confidence-weighted parameter combination. Machine Learning 79(1-2): 123-149 (2010)

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)

Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer: Learning Better Data Representation Using Inference-Driven Metric Learning. ACL (Short Papers) 2010: 377-381

Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jennifer Wortman Vaughan: Regret Minimization With Concept Drift. COLT 2010: 168-180

Avihai Mejer, Koby Crammer: Confidence in Structured-Prediction Using Confidence-Weighted Models. EMNLP 2010: 971-981

Koby Crammer: Efficient online learning with individual learning-rates for phoneme sequence recognition. ICASSP 2010: 4878-4881

Zhuang Wang, Koby Crammer, Slobodan Vucetic: Multi-Class Pegasos on a Budget. ICML 2010: 1143-1150

Koby Crammer, Daniel D. Lee: Learning via Gaussian Herding. NIPS 2010: 451-459

Francesco Orabona, Koby Crammer: New Adaptive Algorithms for Online Classification. NIPS 2010: 1840-1848

Koby Crammer, Mehryar Mohri, Fernando Pereira: Gaussian Margin Machines. Journal of Machine Learning Research - Proceedings Track 5: 105-112 (2009)

Koby Crammer, Mark Dredze, Alex Kulesza: Multi-Class Confidence Weighted Algorithms. EMNLP 2009: 496-504

Ted Sandler, Lyle H. Ungar, Koby Crammer: Resolving Identity Uncertainty with Learned Random Walks. ICDM 2009: 457-465

Hui Lin, Jeff Bilmes, Koby Crammer: How to loose confidence: probabilistic linear machines for multiclass classification. INTERSPEECH 2009: 2559-2562

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

Koby Crammer, Alex Kulesza, Mark Dredze: Adaptive Regularization of Weight Vectors. NIPS 2009: 414-422

Partha Pratim Talukdar, Koby Crammer: New Regularized Algorithms for Transductive Learning. ECML/PKDD (2) 2009: 442-457

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)

Koby Crammer, Michael Kearns, Jennifer Wortman: Learning from Multiple Sources. Journal of Machine Learning Research 9: 1757-1774 (2008)

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)

Koby Crammer: Advanced Online Learning for Natural Language Processing. ACL (Tutorial Abstracts) 2008: 4

Mark Dredze, Koby Crammer: Active Learning with Confidence. ACL (Short Papers) 2008: 233-236

Ron Bekkerman, Koby Crammer: One-Class Clustering in the Text Domain. EMNLP 2008: 41-50

Mark Dredze, Koby Crammer: Online Methods for Multi-Domain Learning and Adaptation. EMNLP 2008: 689-697

Koby Crammer, Partha Pratim Talukdar, Fernando Pereira: A rate-distortion one-class model and its applications to clustering. ICML 2008: 184-191

Mark Dredze, Koby Crammer, Fernando Pereira: Confidence-weighted linear classification. ICML 2008: 264-271

Koby Crammer, Mark Dredze, Fernando Pereira: Exact Convex Confidence-Weighted Learning. NIPS 2008: 345-352

Axel Bernal, Koby Crammer, Artemis G. Hatzigeorgiou, Fernando Pereira: Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction. PLoS Computational Biology 3(3) (2007)

Koby Crammer: A conservative aggressive subspace tracker. INTERSPEECH 2007: 498-501

John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman: Learning Bounds for Domain Adaptation. NIPS 2007

Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer: Online Passive-Aggressive Algorithms. Journal of Machine Learning Research 7: 551-585 (2006)

Linli Xu, Koby Crammer, Dale Schuurmans: Robust Support Vector Machine Training via Convex Outlier Ablation. AAAI 2006: 536-542

Koby Crammer: Online Tracking of Linear Subspaces. COLT 2006: 438-452

Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira: Analysis of Representations for Domain Adaptation. NIPS 2006: 137-144

Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Multiple Sources. NIPS 2006: 321-328

Koby Crammer, Amir Globerson: Discriminative Learning via Semidefinite Probabilistic Models. UAI 2006

Koby Crammer, Yoram Singer: Online Ranking by Projecting. Neural Computation 17(1): 145-175 (2005)

Ryan T. McDonald, Koby Crammer, Fernando C. N. Pereira: Online Large-Margin Training of Dependency Parsers. ACL 2005

Koby Crammer, Yoram Singer: Loss Bounds for Online Category Ranking. COLT 2005: 48-62

Ryan T. McDonald, Koby Crammer, Fernando Pereira: Flexible Text Segmentation with Structured Multilabel Classification. HLT/EMNLP 2005

Koby Crammer, Michael J. Kearns, Jennifer Wortman: Learning from Data of Variable Quality. NIPS 2005

Koby Crammer, Gal Chechik: A needle in a haystack: local one-class optimization. ICML 2004

Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer: A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities. NIPS 2004

Koby Crammer, Yoram Singer: Ultraconservative Online Algorithms for Multiclass Problems. Journal of Machine Learning Research 3: 951-991 (2003)

Koby Crammer, Yoram Singer: A Family of Additive Online Algorithms for Category Ranking. Journal of Machine Learning Research 3: 1025-1058 (2003)

Koby Crammer, Yoram Singer: Learning Algorithm for Enclosing Points in Bregmanian Spheres. COLT 2003: 388-402

Koby Crammer, Jaz S. Kandola, Yoram Singer: Online Classification on a Budget. NIPS 2003

Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer: Online Passive-Aggressive Algorithms. NIPS 2003

Koby Crammer, Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. Machine Learning 47(2-3): 201-233 (2002)

Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby: Margin Analysis of the LVQ Algorithm. NIPS 2002: 462-469

Koby Crammer, Joseph Keshet, Yoram Singer: Kernel Design Using Boosting. NIPS 2002: 537-544

Koby Crammer, Yoram Singer: A new family of online algorithms for category ranking. SIGIR 2002: 151-158

Koby Crammer, Yoram Singer: On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. Journal of Machine Learning Research 2: 265-292 (2001)

Koby Crammer, Yoram Singer: Ultraconservative Online Algorithms for Multiclass Problems. COLT/EuroCOLT 2001: 99-115

Koby Crammer, Yoram Singer: Pranking with Ranking. NIPS 2001: 641-647

Koby Crammer, Yoram Singer: On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000: 35-46

Koby Crammer, Yoram Singer: Improved Output Coding for Classification Using Continuous Relaxation. NIPS 2000: 437-443