Koby Crammer

According to our database, Koby Crammer authored at least 113 papers between 2000 and 2022.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2022

Weighted Training for Cross-Task Learning.

Shuxiao Chen, Koby Crammer, Hangfeng He, Dan Roth, Weijie J. Su

Proceedings of the Tenth International Conference on Learning Representations, 2022

2019

Variance Estimation For Online Regression via Spectrum Thresholding.

Mark Kozdoba, Edward Moroshko, Shie Mannor, Koby Crammer

CoRR, 2019

2018

Discriminative Keyword Spotting for limited-data applications.

Hadas Benisty, Itamar Katz, Koby Crammer, David Malah

Speech Commun., 2018

Multi Instance Learning For Unbalanced Data.

Mark Kozdoba, Edward Moroshko, Lior Shani, Takuya Takagi, Takashi Katoh, Shie Mannor, Koby Crammer

CoRR, 2018

Efficient Loss-Based Decoding on Graphs for Extreme Classification.

Itay Evron, Edward Moroshko, Koby Crammer

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

A Better Resource Allocation Algorithm with Semi-Bandit Feedback.

Yuval Dagan, Koby Crammer

Proceedings of the Algorithmic Learning Theory, 2018

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

Online Regression with Controlled Label Noise Rate.

Edward Moroshko, Koby Crammer

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2017

Rotting Bandits.

Nir Levine, Koby Crammer, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

2016

Grid-based approximation for voice conversion in low resource environments.

Hadas Benisty, David Malah, Koby Crammer

EURASIP J. Audio Speech Music. Process., 2016

Bandits meet Computer Architecture: Designing a Smartly-allocated Cache.

Yonatan Glassner, Koby Crammer

CoRR, 2016

That's Not My Question: Learning to Weight Unmatched Terms in CQA Vertical Search.

Boaz Petersil, Avihai Mejer, Idan Szpektor, Koby Crammer

Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval, 2016

2015

A generalized online mirror descent with applications to classification and regression.

Francesco Orabona, Koby Crammer, Nicolò Cesa-Bianchi

Mach. Learn., 2015

Second-order non-stationary online learning for regression.

Edward Moroshko, Nina Vaits, Koby Crammer

J. Mach. Learn. Res., 2015

CONQUER: Confusion Queried Online Bandit Learning.

Daniel Barsky, Koby Crammer

CoRR, 2015

Linear Multi-Resource Allocation with Semi-Bandit Feedback.

Tor Lattimore, Koby Crammer, Csaba Szepesvári

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

Belief flows for robust online learning.

Pedro A. Ortega, Koby Crammer, Daniel D. Lee

Proceedings of the 2015 Information Theory and Applications Workshop, 2015

In-memory hamming similarity computation in resistive arrays.

Yuval Cassuto, Koby Crammer

Proceedings of the IEEE International Symposium on Information Theory, 2015

Convex Multi-Task Learning by Clustering.

Aviad Barzilai, Koby Crammer

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Outlier-Robust Convex Segmentation.

Itamar Katz, Koby Crammer

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Weighted last-step min-max algorithm with improved sub-logarithmic regret.

Edward Moroshko, Koby Crammer

Theor. Comput. Sci., 2014

Optimal Resource Allocation with Semi-Bandit Feedback.

Tor Lattimore, Koby Crammer, Csaba Szepesvári

Proceedings of the Thirtieth Conference on Uncertainty in Artificial Intelligence, 2014

Learning Multiple Tasks in Parallel with a Shared Annotator.

Haim Cohen, Koby Crammer

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Prediction with Limited Advice and Multiarmed Bandits with Paid Observations.

Yevgeny Seldin, Peter L. Bartlett, Koby Crammer, Yasin Abbasi-Yadkori

Proceedings of the 31th International Conference on Machine Learning, 2014

Concept Drift Detection Through Resampling.

Maayan Harel, Shie Mannor, Ran El-Yaniv, Koby Crammer

Proceedings of the 31th International Conference on Machine Learning, 2014

Non-parallel voice conversion using joint optimization of alignment by temporal context and spectral distortion.

Hadas Benisty, David Malah, Koby Crammer

Proceedings of the IEEE International Conference on Acoustics, 2014

Robust Forward Algorithms via PAC-Bayes and Laplace Distributions.

Asaf Noy, Koby Crammer

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Selective Sampling with Drift.

Edward Moroshko, Koby Crammer

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

Doubly Aggressive Selective Sampling Algorithms for Classification.

Koby Crammer

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Adaptive regularization of weight vectors.

Koby Crammer, Alex Kulesza, Mark Dredze

Mach. Learn., 2013

Multiclass classification with bandit feedback using adaptive regularization.

Koby Crammer, Claudio Gentile

Mach. Learn., 2013

Advice-Efficient Prediction with Expert Advice

Yevgeny Seldin, Peter L. Bartlett, Koby Crammer

CoRR, 2013

Multi Class Learning with Individual Sparsity.

Ben Zion Vatashsky, Koby Crammer

Proceedings of the IJCAI 2013, 2013

Hartigan's K-Means Versus Lloyd's K-Means - Is It Time for a Change?

Noam Slonim, Ehud Aharoni, Koby Crammer

Proceedings of the IJCAI 2013, 2013

Open Problem: Adversarial Multiarmed Bandits with Limited Advice.

Yevgeny Seldin, Koby Crammer, Peter L. Bartlett

Proceedings of the COLT 2013, 2013

A Last-Step Regression Algorithm for Non-Stationary Online Learning.

Edward Moroshko, Koby Crammer

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

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 Simple Geometric Interpretation of SVM using Stochastic Adversaries.

Roi Livni, Koby Crammer, Amir Globerson

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

More Is Better: Large Scale Partially-supervised Sentiment Classication.

Yoav Haimovitch, Koby Crammer, Shie Mannor

Proceedings of the 4th Asian Conference on Machine Learning, 2012

Confidence-Weighted Linear Classification for Text Categorization.

Koby Crammer, Mark Dredze, Fernando Pereira

J. Mach. Learn. Res., 2012

More Is Better: Large Scale Partially-supervised Sentiment Classification - Appendix

Yoav Haimovitch, Koby Crammer, Shie Mannor

CoRR, 2012

Adaptive Regularization for Weight Matrices

Koby Crammer, Gal Chechik

CoRR, 2012

Automated gene-model curation using global discriminative learning.

Axel Bernal, Koby Crammer, Fernando Pereira

Bioinform., 2012

Graph-Based Transduction with Confidence.

Matan Orbach, Koby Crammer

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2012

Volume Regularization for Binary Classification.

Koby Crammer, Tal Wagner

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Learning Multiple Tasks using Shared Hypotheses.

Koby Crammer, Yishay Mansour

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Are You Sure? Confidence in Prediction of Dependency Tree Edges.

Avihai Mejer, Koby Crammer

Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2012

Training Dependency Parser Using Light Feedback.

Avihai Mejer, Koby Crammer

Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, 2012

Adaptive Regularization for Similarity Measures.

Koby Crammer, Gal Chechik

Proceedings of the 29th International Conference on Machine Learning, 2012

Online discriminative learning of phoneme recognition via collections of generalized linear models.

Koby Crammer, Daniel D. Lee

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

New ℌ^∞ bounds for the recursive least squares algorithm exploiting input structure.

Koby Crammer, Alex Kulesza, Mark Dredze

Proceedings of the 2012 IEEE International Conference on Acoustics, 2012

Modular Global Variance enhancement for voice conversion systems.

Hadas Benisty, David Malah, Koby Crammer

Proceedings of the 20th European Signal Processing Conference, 2012

Metric Learning for Graph-Based Domain Adaptation.

Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer

Proceedings of the COLING 2012, 2012

2011

Confidence Estimation in Structured Prediction

Avihai Mejer, Koby Crammer

CoRR, 2011

Trading representability for scalability: adaptive multi-hyperplane machine for nonlinear classification.

Zhuang Wang, Nemanja Djuric, Koby Crammer, Slobodan Vucetic

Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2011

Active Online Classification via Information Maximization.

Noam Slonim, Elad Yom-Tov, Koby Crammer

Proceedings of the IJCAI 2011, 2011

Re-adapting the Regularization of Weights for Non-stationary Regression.

Nina Vaits, Koby Crammer

Proceedings of the Algorithmic Learning Theory - 22nd International Conference, 2011

2010

Multi-domain learning by confidence-weighted parameter combination.

Mark Dredze, Alex Kulesza, Koby Crammer

Mach. Learn., 2010

A theory of learning from different domains.

Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman Vaughan

Mach. Learn., 2010

Exploiting Feature Covariance in High-Dimensional Online Learning.

Justin Ma, Alex Kulesza, Mark Dredze, Koby Crammer, Lawrence K. Saul, Fernando Pereira

Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, 2010

New Adaptive Algorithms for Online Classification.

Francesco Orabona, Koby Crammer

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Learning via Gaussian Herding.

Koby Crammer, Daniel D. Lee

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

Multi-Class Pegasos on a Budget.

Zhuang Wang, Koby Crammer, Slobodan Vucetic

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

Efficient online learning with individual learning-rates for phoneme sequence recognition.

Koby Crammer

Proceedings of the IEEE International Conference on Acoustics, 2010

Confidence in Structured-Prediction Using Confidence-Weighted Models.

Avihai Mejer, Koby Crammer

Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 2010

Regret Minimization With Concept Drift.

Koby Crammer, Yishay Mansour, Eyal Even-Dar, Jennifer Wortman Vaughan

Proceedings of the COLT 2010, 2010

Learning Better Data Representation Using Inference-Driven Metric Learning.

Paramveer S. Dhillon, Partha Pratim Talukdar, Koby Crammer

Proceedings of the ACL 2010, 2010

2009

Gaussian Margin Machines.

Koby Crammer, Mehryar Mohri, Fernando Pereira

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

New Regularized Algorithms for Transductive Learning.

Partha Pratim Talukdar, Koby Crammer

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2009

Loss-Sensitive Discriminative Training of Machine Transliteration Models.

Kedar Bellare, Koby Crammer, Dayne Freitag

Proceedings of the Human Language Technologies: Conference of the North American Chapter of the Association of Computational Linguistics, Proceedings, May 31, 2009

How to loose confidence: probabilistic linear machines for multiclass classification.

Hui Lin, Jeff A. Bilmes, Koby Crammer

Proceedings of the 10th Annual Conference of the International Speech Communication Association, 2009

Resolving Identity Uncertainty with Learned Random Walks.

Ted Sandler, Lyle H. Ungar, Koby Crammer

Proceedings of the ICDM 2009, 2009

Multi-Class Confidence Weighted Algorithms.

Koby Crammer, Mark Dredze, Alex Kulesza

Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, 2009

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

Learning from Multiple Sources.

Koby Crammer, Michael J. Kearns, Jennifer Wortman

J. Mach. Learn. Res., 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

Exact Convex Confidence-Weighted Learning.

Koby Crammer, Mark Dredze, Fernando Pereira

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Confidence-weighted linear classification.

Mark Dredze, Koby Crammer, Fernando Pereira

Proceedings of the Machine Learning, 2008

A rate-distortion one-class model and its applications to clustering.

Koby Crammer, Partha Pratim Talukdar, Fernando C. N. Pereira

Proceedings of the Machine Learning, 2008

Online Methods for Multi-Domain Learning and Adaptation.

Mark Dredze, Koby Crammer

Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, 2008

One-Class Clustering in the Text Domain.

Ron Bekkerman, Koby Crammer

Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, 2008

Active Learning with Confidence.

Mark Dredze, Koby Crammer

Proceedings of the ACL 2008, 2008

Advanced Online Learning for Natural Language Processing.

Koby Crammer

Proceedings of the ACL 2008, 2008

2007

Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction.

Axel Bernal, Koby Crammer, Artemis G. Hatzigeorgiou, Fernando Pereira

PLoS Comput. Biol., 2007

Learning Bounds for Domain Adaptation.

John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, Jennifer Wortman

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

A conservative aggressive subspace tracker.

Koby Crammer

Proceedings of the 8th Annual Conference of the International Speech Communication Association, 2007

Automatic Code Assignment to Medical Text.

Koby Crammer, Mark Dredze, Kuzman Ganchev, Partha Pratim Talukdar, Steven Carroll

Proceedings of the Biological, translational, and clinical language processing, 2007

2006

Online Passive-Aggressive Algorithms.

Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer

J. Mach. Learn. Res., 2006

Discriminative Learning via Semidefinite Probabilistic Models.

Koby Crammer, Amir Globerson

Proceedings of the UAI '06, 2006

Analysis of Representations for Domain Adaptation.

Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Room Impulse Response Estimation using Sparse Online Prediction and Absolute Loss.

Koby Crammer, Daniel D. Lee

Proceedings of the 2006 IEEE International Conference on Acoustics Speech and Signal Processing, 2006

Online Tracking of Linear Subspaces.

Koby Crammer

Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

Robust Support Vector Machine Training via Convex Outlier Ablation.

Linli Xu, Koby Crammer, Dale Schuurmans

Proceedings of the Proceedings, 2006

2005

Online Ranking by Projecting.

Koby Crammer, Yoram Singer

Neural Comput., 2005

Learning from Data of Variable Quality.

Koby Crammer, Michael J. Kearns, Jennifer Wortman

Proceedings of the Advances in Neural Information Processing Systems 18 [Neural Information Processing Systems, 2005

Flexible Text Segmentation with Structured Multilabel Classification.

Ryan T. McDonald, Koby Crammer, Fernando Pereira

Proceedings of the HLT/EMNLP 2005, 2005

Loss Bounds for Online Category Ranking.

Koby Crammer, Yoram Singer

Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

Online Large-Margin Training of Dependency Parsers.

Ryan T. McDonald, Koby Crammer, Fernando C. N. Pereira

Proceedings of the ACL 2005, 2005

2004

Online learning of complex categorical problems (למידה מקוונת של בעיות דירוג מורכבות.).

Koby Crammer

PhD thesis, 2004

A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities.

Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

A needle in a haystack: local one-class optimization.

Koby Crammer, Gal Chechik

Proceedings of the Machine Learning, 2004

2003

A Family of Additive Online Algorithms for Category Ranking.

Koby Crammer, Yoram Singer

J. Mach. Learn. Res., 2003

Ultraconservative Online Algorithms for Multiclass Problems.

Koby Crammer, Yoram Singer

J. Mach. Learn. Res., 2003

Online Passive-Aggressive Algorithms.

Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Online Classification on a Budget.

Koby Crammer, Jaz S. Kandola, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Learning Algorithm for Enclosing Points in Bregmanian Spheres.

Koby Crammer, Yoram Singer

Proceedings of the Computational Learning Theory and Kernel Machines, 2003

2002

On the Learnability and Design of Output Codes for Multiclass Problems.

Koby Crammer, Yoram Singer

Mach. Learn., 2002

A new family of online algorithms for category ranking.

Koby Crammer, Yoram Singer

Proceedings of the SIGIR 2002: Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2002

Kernel Design Using Boosting.

Koby Crammer, Joseph Keshet, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Margin Analysis of the LVQ Algorithm.

Koby Crammer, Ran Gilad-Bachrach, Amir Navot, Naftali Tishby

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

2001

On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines.

Koby Crammer, Yoram Singer

J. Mach. Learn. Res., 2001

Pranking with Ranking.

Koby Crammer, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

2000

Improved Output Coding for Classification Using Continuous Relaxation.

Koby Crammer, Yoram Singer

Proceedings of the Advances in Neural Information Processing Systems 13, 2000