Improved Output Coding for Classification Using Continuous Relaxation. - Koby Crammer; Yoram Singer (NIPS, 2000)

Pranking with Ranking. - Koby Crammer; Yoram Singer (NIPS, 2001)

On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. - Koby Crammer; Yoram Singer (J. Mach. Learn. Res., 2001)

Margin Analysis of the LVQ Algorithm. - Koby Crammer; Ran Gilad-Bachrach; Amir Navot; Naftali Tishby (NIPS, 2002)

Kernel Design Using Boosting. - Koby Crammer; Joseph Keshet; Yoram Singer (NIPS, 2002)

A new family of online algorithms for category ranking. - Koby Crammer; Yoram Singer (SIGIR, 2002)

On the Learnability and Design of Output Codes for Multiclass Problems. - Koby Crammer; Yoram Singer (Mach. Learn., 2002)

Learning Algorithm for Enclosing Points in Bregmanian Spheres. - Koby Crammer; Yoram Singer (COLT, 2003)

Online Classification on a Budget. - Koby Crammer; Jaz S. Kandola; Yoram Singer (NIPS, 2003)

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

Ultraconservative Online Algorithms for Multiclass Problems. - Koby Crammer; Yoram Singer (J. Mach. Learn. Res., 2003)

A Family of Additive Online Algorithms for Category Ranking. - Koby Crammer; Yoram Singer (J. Mach. Learn. Res., 2003)

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

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

Online learning of complex categorical problems (למידה מקוונת של בעיות דירוג מורכבות.). - Koby Crammer (2004)

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

Loss Bounds for Online Category Ranking. - Koby Crammer; Yoram Singer (COLT, 2005)

Flexible Text Segmentation with Structured Multilabel Classification. - Ryan T. McDonald; Koby Crammer; Fernando Pereira 003 (HLT/EMNLP, 2005)

Learning from Data of Variable Quality. - Koby Crammer; Michael J. Kearns; Jennifer Wortman (NIPS, 2005)

Online Ranking by Projecting. - Koby Crammer; Yoram Singer (Neural Comput., 2005)

Robust Support Vector Machine Training via Convex Outlier Ablation. - Linli Xu; Koby Crammer; Dale Schuurmans (AAAI, 2006)

Online Tracking of Linear Subspaces. - Koby Crammer (COLT, 2006)

Room Impulse Response Estimation using Sparse Online Prediction and Absolute Loss. - Koby Crammer; Daniel D. Lee (ICASSP (3), 2006)

Analysis of Representations for Domain Adaptation. - Shai Ben-David; John Blitzer; Koby Crammer; Fernando Pereira 003 (NIPS, 2006)

Discriminative Learning via Semidefinite Probabilistic Models. - Koby Crammer; Amir Globerson (UAI, 2006)

Online Passive-Aggressive Algorithms. - Koby Crammer; Ofer Dekel; Joseph Keshet; Shai Shalev-Shwartz; Yoram Singer (J. Mach. Learn. Res., 2006)

Automatic Code Assignment to Medical Text. - Koby Crammer; Mark Dredze; Kuzman Ganchev; Partha Pratim Talukdar; Steven Carroll (BioNLP@ACL, 2007)

A conservative aggressive subspace tracker. - Koby Crammer (INTERSPEECH, 2007)

Learning Bounds for Domain Adaptation. - John Blitzer; Koby Crammer; Alex Kulesza; Fernando Pereira 003; Jennifer Wortman (NIPS, 2007)

Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction. - Axel Bernal; Koby Crammer; Artemis G. Hatzigeorgiou; Fernando Pereira 003 (PLoS Comput. Biol., 2007)

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

Active Learning with Confidence. - Mark Dredze; Koby Crammer (ACL (2), 2008)

One-Class Clustering in the Text Domain. - Ron Bekkerman; Koby Crammer (EMNLP, 2008)

Online Methods for Multi-Domain Learning and Adaptation. - Mark Dredze; Koby Crammer (EMNLP, 2008)

A rate-distortion one-class model and its applications to clustering. - Koby Crammer; Partha Pratim Talukdar; Fernando C. N. Pereira (ICML, 2008)

Confidence-weighted linear classification. - Mark Dredze; Koby Crammer; Fernando Pereira 003 (ICML, 2008)

Exact Convex Confidence-Weighted Learning. - Koby Crammer; Mark Dredze; Fernando Pereira 003 (NIPS, 2008)

Reranking candidate gene models with cross-species comparison for improved gene prediction. - Qian Liu; Koby Crammer; Fernando C. N. Pereira; David S. Roos (BMC Bioinform., 2008)

Learning from Multiple Sources. - Koby Crammer; Michael J. Kearns; Jennifer Wortman (J. Mach. Learn. Res., 2008)

Learning to create data-integrating queries. - Partha Pratim Talukdar; Marie Jacob; Muhammad Salman Mehmood; Koby Crammer; Zachary G. Ives; Fernando C. N. Pereira; Sudipto Guha (Proc. VLDB Endow., 2008)

Multi-Class Confidence Weighted Algorithms. - Koby Crammer; Mark Dredze; Alex Kulesza (EMNLP, 2009)

Resolving Identity Uncertainty with Learned Random Walks. - Ted Sandler; Lyle H. Ungar; Koby Crammer (ICDM, 2009)

How to loose confidence: probabilistic linear machines for multiclass classification. - Hui Lin 001; Jeff A. Bilmes; Koby Crammer (INTERSPEECH, 2009)

Loss-Sensitive Discriminative Training of Machine Transliteration Models. - Kedar Bellare; Koby Crammer; Dayne Freitag (HLT-NAACL (Student Research Workshop and Doctoral Consortium), 2009)

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

Gaussian Margin Machines. - Koby Crammer; Mehryar Mohri; Fernando Pereira 003 (AISTATS, 2009)

Learning Better Data Representation Using Inference-Driven Metric Learning. - Paramveer S. Dhillon; Partha Pratim Talukdar; Koby Crammer (ACL (2), 2010)

Regret Minimization With Concept Drift. - Koby Crammer; Yishay Mansour; Eyal Even-Dar; Jennifer Wortman Vaughan (COLT, 2010)

Confidence in Structured-Prediction Using Confidence-Weighted Models. - Avihai Mejer; Koby Crammer (EMNLP, 2010)

Efficient online learning with individual learning-rates for phoneme sequence recognition. - Koby Crammer (ICASSP, 2010)

Multi-Class Pegasos on a Budget. - Zhuang Wang; Koby Crammer; Slobodan Vucetic (ICML, 2010)

Learning via Gaussian Herding. - Koby Crammer; Daniel D. Lee (NIPS, 2010)

New Adaptive Algorithms for Online Classification. - Francesco Orabona; Koby Crammer (NIPS, 2010)

Exploiting Feature Covariance in High-Dimensional Online Learning. - Justin Ma; Alex Kulesza; Mark Dredze; Koby Crammer; Lawrence K. Saul; Fernando Pereira 003 (AISTATS, 2010)

A theory of learning from different domains. - Shai Ben-David; John Blitzer; Koby Crammer; Alex Kulesza; Fernando Pereira 003; Jennifer Wortman Vaughan (Mach. Learn., 2010)

Multi-domain learning by confidence-weighted parameter combination. - Mark Dredze; Alex Kulesza; Koby Crammer (Mach. Learn., 2010)

Re-adapting the Regularization of Weights for Non-stationary Regression. - Nina Vaits; Koby Crammer (ALT, 2011)

Active Online Classification via Information Maximization. - Noam Slonim; Elad Yom-Tov; Koby Crammer (IJCAI, 2011)

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

Confidence Estimation in Structured Prediction - Avihai Mejer; Koby Crammer (CoRR, 2011)

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

Modular Global Variance enhancement for voice conversion systems. - Hadas Benisty; David Malah; Koby Crammer (EUSIPCO, 2012)

New ℌ∞ bounds for the recursive least squares algorithm exploiting input structure. - Koby Crammer; Alex Kulesza; Mark Dredze (ICASSP, 2012)

Online discriminative learning of phoneme recognition via collections of generalized linear models. - Koby Crammer; Daniel D. Lee (ICASSP, 2012)

Adaptive Regularization for Similarity Measures. - Koby Crammer; Gal Chechik (ICML, 2012)

Training Dependency Parser Using Light Feedback. - Avihai Mejer; Koby Crammer (HLT-NAACL, 2012)

Are You Sure? Confidence in Prediction of Dependency Tree Edges. - Avihai Mejer; Koby Crammer (HLT-NAACL, 2012)

Learning Multiple Tasks using Shared Hypotheses. - Koby Crammer; Yishay Mansour (NIPS, 2012)

Volume Regularization for Binary Classification. - Koby Crammer; Tal Wagner (NIPS, 2012)

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

Automated gene-model curation using global discriminative learning. - Axel Bernal; Koby Crammer; Fernando Pereira 003 (Bioinform., 2012)

Adaptive Regularization for Weight Matrices - Koby Crammer; Gal Chechik (CoRR, 2012)

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

Confidence-Weighted Linear Classification for Text Categorization. - Koby Crammer; Mark Dredze; Fernando Pereira 003 (J. Mach. Learn. Res., 2012)

More Is Better: Large Scale Partially-supervised Sentiment Classication. - Yoav Haimovitch; Koby Crammer; Shie Mannor (ACML, 2012)

A Simple Geometric Interpretation of SVM using Stochastic Adversaries. - Roi Livni; Koby Crammer; Amir Globerson (AISTATS, 2012)

Breaking the curse of kernelization: budgeted stochastic gradient descent for large-scale SVM training. - Zhuang Wang; Koby Crammer; Slobodan Vucetic (J. Mach. Learn. Res., 2012)

A Last-Step Regression Algorithm for Non-Stationary Online Learning. - Edward Moroshko; Koby Crammer (AISTATS, 2013)

Open Problem: Adversarial Multiarmed Bandits with Limited Advice. - Yevgeny Seldin; Koby Crammer; Peter L. Bartlett (COLT, 2013)

Hartigan's K-Means Versus Lloyd's K-Means - Is It Time for a Change? - Noam Slonim; Ehud Aharoni; Koby Crammer (IJCAI, 2013)

Multi Class Learning with Individual Sparsity. - Ben Zion Vatashsky; Koby Crammer (IJCAI, 2013)

Advice-Efficient Prediction with Expert Advice - Yevgeny Seldin; Peter L. Bartlett; Koby Crammer (CoRR, 2013)

Multiclass classification with bandit feedback using adaptive regularization. - Koby Crammer; Claudio Gentile (Mach. Learn., 2013)

Adaptive regularization of weight vectors. - Koby Crammer; Alex Kulesza; Mark Dredze (Mach. Learn., 2013)

Doubly Aggressive Selective Sampling Algorithms for Classification. - Koby Crammer (AISTATS, 2014)

Selective Sampling with Drift. - Edward Moroshko; Koby Crammer (AISTATS, 2014)

Robust Forward Algorithms via PAC-Bayes and Laplace Distributions. - Asaf Noy; Koby Crammer (AISTATS, 2014)

Non-parallel voice conversion using joint optimization of alignment by temporal context and spectral distortion. - Hadas Benisty; David Malah; Koby Crammer (ICASSP, 2014)

Concept Drift Detection Through Resampling. - Maayan Harel; Shie Mannor; Ran El-Yaniv; Koby Crammer (ICML, 2014)

Prediction with Limited Advice and Multiarmed Bandits with Paid Observations. - Yevgeny Seldin; Peter L. Bartlett; Koby Crammer; Yasin Abbasi-Yadkori (ICML, 2014)

Learning Multiple Tasks in Parallel with a Shared Annotator. - Haim Cohen; Koby Crammer (NIPS, 2014)

Optimal Resource Allocation with Semi-Bandit Feedback. - Tor Lattimore; Koby Crammer; Csaba Szepesvári (UAI, 2014)

Weighted last-step min-max algorithm with improved sub-logarithmic regret. - Edward Moroshko; Koby Crammer (Theor. Comput. Sci., 2014)

Outlier-Robust Convex Segmentation. - Itamar Katz; Koby Crammer (AAAI, 2015)

Convex Multi-Task Learning by Clustering. - Aviad Barzilai; Koby Crammer (AISTATS, 2015)

In-memory hamming similarity computation in resistive arrays. - Yuval Cassuto; Koby Crammer (ISIT, 2015)

Belief flows for robust online learning. - Pedro A. Ortega; Koby Crammer; Daniel D. Lee (ITA, 2015)

Linear Multi-Resource Allocation with Semi-Bandit Feedback. - Tor Lattimore; Koby Crammer; Csaba Szepesvári (NIPS, 2015)

CONQUER: Confusion Queried Online Bandit Learning. - Daniel Barsky; Koby Crammer (CoRR, 2015)

Second-order non-stationary online learning for regression. - Edward Moroshko; Nina Vaits; Koby Crammer (J. Mach. Learn. Res., 2015)

A generalized online mirror descent with applications to classification and regression. - Francesco Orabona; Koby Crammer; Nicolò Cesa-Bianchi (Mach. Learn., 2015)

That's Not My Question: Learning to Weight Unmatched Terms in CQA Vertical Search. - Boaz Petersil; Avihai Mejer; Idan Szpektor; Koby Crammer (SIGIR, 2016)

Bandits meet Computer Architecture: Designing a Smartly-allocated Cache. - Yonatan Glassner; Koby Crammer (CoRR, 2016)

Grid-based approximation for voice conversion in low resource environments. - Hadas Benisty; David Malah; Koby Crammer (EURASIP J. Audio Speech Music. Process., 2016)

Rotting Bandits. - Nir Levine; Koby Crammer; Shie Mannor (NIPS, 2017)

Online Regression with Controlled Label Noise Rate. - Edward Moroshko; Koby Crammer (ECML/PKDD (2), 2017)

Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random Forests. - Noam Segev; Maayan Harel; Shie Mannor; Koby Crammer; Ran El-Yaniv (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

A Better Resource Allocation Algorithm with Semi-Bandit Feedback. - Yuval Dagan; Koby Crammer (ALT, 2018)

Efficient Loss-Based Decoding on Graphs for Extreme Classification. - Itay Evron; Edward Moroshko; Koby Crammer (NeurIPS, 2018)

Multi Instance Learning For Unbalanced Data. - Mark Kozdoba; Edward Moroshko; Lior Shani; Takuya Takagi; Takashi Katoh; Shie Mannor; Koby Crammer (CoRR, 2018)

Discriminative Keyword Spotting for limited-data applications. - Hadas Benisty; Itamar Katz; Koby Crammer; David Malah (Speech Commun., 2018)

Variance Estimation For Online Regression via Spectrum Thresholding. - Mark Kozdoba; Edward Moroshko; Shie Mannor; Koby Crammer (CoRR, 2019)

Weighted Training for Cross-Task Learning. - Shuxiao Chen; Koby Crammer; Hangfeng He 001; Dan Roth 001; Weijie J. Su (ICLR, 2022)