Yoram Singer

Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer: Matrix Approximation under Local Low-Rank Assumption. CoRR abs/1301.3192 (2013)

Yoram Singer: Switching Portfolios. CoRR abs/1301.7413 (2013)

Eric Bauer, Daphne Koller, Yoram Singer: Update Rules for Parameter Estimation in Bayesian Networks. CoRR abs/1302.1519 (2013)

John C. Duchi, Elad Hazan, Yoram Singer: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. Journal of Machine Learning Research 12: 2121-2159 (2011)

Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter: Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1): 3-30 (2011)

Moshe Dubiner, Yoram Singer: Entire Relaxation Path for Maximum Entropy Problems. EMNLP 2011: 941-948

William W. Cohen, Robert E. Schapire, Yoram Singer: Learning to Order Things. CoRR abs/1105.5464 (2011)

Shai Shalev-Shwartz, Yoram Singer: On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms. Machine Learning 80(2-3): 141-163 (2010)

John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari: Composite Objective Mirror Descent. COLT 2010: 14-26

John C. Duchi, Elad Hazan, Yoram Singer: Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. COLT 2010: 257-269

John C. Duchi, Yoram Singer: Efficient Online and Batch Learning Using Forward Backward Splitting. Journal of Machine Learning Research 10: 2899-2934 (2009)

Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer: Individual sequence prediction using memory-efficient context trees. IEEE Transactions on Information Theory 55(11): 5251-5262 (2009)

John C. Duchi, Yoram Singer: Boosting with structural sparsity. ICML 2009: 38

Samy Bengio, Fernando C. N. Pereira, Yoram Singer, Dennis Strelow: Group Sparse Coding. NIPS 2009: 82-89

John C. Duchi, Yoram Singer: Efficient Learning using Forward-Backward Splitting. NIPS 2009: 495-503

Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer: Online Learning of Complex Prediction Problems Using Simultaneous Projections. Journal of Machine Learning Research 9: 1399-1435 (2008)

Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer: The Forgetron: A Kernel-Based Perceptron on a Budget. SIAM J. Comput. 37(5): 1342-1372 (2008)

Shai Shalev-Shwartz, Yoram Singer: On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms. COLT 2008: 311-322

John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Tushar Chandra: Efficient projections onto the l₁-ball for learning in high dimensions. ICML 2008: 272-279

John C. Platt, Daphne Koller, Yoram Singer, Sam T. Roweis (Eds.): Advances in Neural Information Processing Systems 20, Proceedings of the Twenty-First Annual Conference on Neural Information Processing Systems, Vancouver, British Columbia, Canada, December 3-6, 2007. Curran Associates, Inc. 2008

Yonatan Amit, Ofer Dekel, Yoram Singer: A Boosting Algorithm for Label Covering in Multilabel Problems. Journal of Machine Learning Research - Proceedings Track 2: 27-34 (2007)

Shai Shalev-Shwartz, Yoram Singer: A Unified Algorithmic Approach for Efficient Online Label Ranking. Journal of Machine Learning Research - Proceedings Track 2: 452-459 (2007)

Ofer Dekel, Philip M. Long, Yoram Singer: Online Learning of Multiple Tasks with a Shared Loss. Journal of Machine Learning Research 8: 2233-2264 (2007)

Shai Shalev-Shwartz, Yoram Singer: A primal-dual perspective of online learning algorithms. Machine Learning 69(2-3): 115-142 (2007)

Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan: A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment. IEEE Transactions on Audio, Speech & Language Processing 15(8): 2373-2382 (2007)

Andrea Frome, Yoram Singer, Fei Sha, Jitendra Malik: Learning Globally-Consistent Local Distance Functions for Shape-Based Image Retrieval and Classification. ICCV 2007: 1-8

Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. ICML 2007: 807-814

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

Shai Shalev-Shwartz, Yoram Singer: Efficient Learning of Label Ranking by Soft Projections onto Polyhedra. Journal of Machine Learning Research 7: 1567-1599 (2006)

Shai Shalev-Shwartz, Yoram Singer: Online Learning Meets Optimization in the Dual. COLT 2006: 423-437

Ofer Dekel, Philip M. Long, Yoram Singer: Online Multitask Learning. COLT 2006: 453-467

Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman: Online multiclass learning by interclass hypothesis sharing. ICML 2006: 313-320

Joseph Keshet, Shai Shalev-Shwartz, Samy Bengio, Yoram Singer, Dan Chazan: Discriminative kernel-based phoneme sequence recognition. INTERSPEECH 2006

Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer: Online Classification for Complex Problems Using Simultaneous Projections. NIPS 2006: 17-24

Ofer Dekel, Yoram Singer: Support Vector Machines on a Budget. NIPS 2006: 345-352

Andrea Frome, Yoram Singer, Jitendra Malik: Image Retrieval and Classification Using Local Distance Functions. NIPS 2006: 417-424

Shai Shalev-Shwartz, Yoram Singer: Convex Repeated Games and Fenchel Duality. NIPS 2006: 1265-1272

Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer: Smooth epsiloon-Insensitive Regression by Loss Symmetrization. Journal of Machine Learning Research 6: 711-741 (2005)

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

Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia: Spikernels: Predicting Arm Movements by Embedding Population Spike Rate Patterns in Inner-Product Spaces. Neural Computation 17(3): 671-690 (2005)

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

Shai Shalev-Shwartz, Yoram Singer: A New Perspective on an Old Perceptron Algorithm. COLT 2005: 264-278

Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan: Phoneme alignment based on discriminative learning. INTERSPEECH 2005: 2961-2964

Ofer Dekel, Yoram Singer: Data-Driven Online to Batch Conversions. NIPS 2005

Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer: The Forgetron: A Kernel-Based Perceptron on a Fixed Budget. NIPS 2005

Ofer Dekel, Joseph Keshet, Yoram Singer: Large margin hierarchical classification. ICML 2004

Nir Krause, Yoram Singer: Leveraging the margin more carefully. ICML 2004

Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng: Online and batch learning of pseudo-metrics. ICML 2004

Shai Shalev-Shwartz, Joseph Keshet, Yoram Singer: Learning to Align Polyphonic Music. ISMIR 2004

Ofer Dekel, Joseph Keshet, Yoram Singer: An Online Algorithm for Hierarchical Phoneme Classification. MLMI 2004: 146-158

Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer: The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees. NIPS 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

John Shawe-Taylor, Yoram Singer (Eds.): Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings. Lecture Notes in Computer Science 3120, Springer 2004, isbn 3-540-22282-0

Eleazar Eskin, William Stafford Noble, Yoram Singer: Protein Family Classification Using Sparse Markov Transducers. Journal of Computational Biology 10(2): 187-213 (2003)

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)

Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer: An Efficient Boosting Algorithm for Combining Preferences. Journal of Machine Learning Research 4: 933-969 (2003)

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

Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer: Smooth e-Intensive Regression by Loss Symmetrization. COLT 2003: 433-447

Kristina Toutanova, Dan Klein, Christopher D. Manning, Yoram Singer: Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. HLT-NAACL 2003

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

Ofer Dekel, Christopher D. Manning, Yoram Singer: Log-Linear Models for Label Ranking. NIPS 2003

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

Eleazar Eskin, William Stafford Noble, Yoram Singer: Using Substitution Matrices to Estimate Probability Distributions for Biological Sequences. Journal of Computational Biology 9(6): 775-791 (2002)

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

Michael Collins, Robert E. Schapire, Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. Machine Learning 48(1-3): 253-285 (2002)

Sanjoy Dasgupta, Elan Pavlov, Yoram Singer: An Efficient PAC Algorithm for Reconstructing a Mixture of Lines. ALT 2002: 351-364

Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia: Spikernels: Embedding Spiking Neurons in Inner-Product Spaces. NIPS 2002: 125-132

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

Ofer Dekel, Yoram Singer: Multiclass Learning by Probabilistic Embeddings. NIPS 2002: 945-952

Ehud Ben-Reuven, Yoram Singer: Discriminative Binaural Sound Localization. NIPS 2002: 1229-1236

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

Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer: Robust temporal and spectral modeling for query By melody. SIGIR 2002: 331-338

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

Yoram Singer: Guest Editor's Introduction. Machine Learning 43(3): 71-172 (2001)

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

Eleazar Eskin, William Noble Grundy, Yoram Singer: Using mixtures of common ancestors for estimating the probabilities of discrete events in biological sequences. ISMB (Supplement of Bioinformatics) 2001: 65-73

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

Erin L. Allwein, Robert E. Schapire, Yoram Singer: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning Research 1: 113-141 (2000)

Robert E. Schapire, Yoram Singer: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39(2/3): 135-168 (2000)

Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal: Boosting for Document Routing. CIKM 2000: 70-77

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

Michael Collins, Robert E. Schapire, Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. COLT 2000: 158-169

Erin L. Allwein, Robert E. Schapire, Yoram Singer: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. ICML 2000: 9-16

Peter Ju, Leslie Pack Kaelbling, Yoram Singer: State-based Classification of Finger Gestures from Electromyographic Signals. ICML 2000: 439-446

Eleazar Eskin, William Noble Grundy, Yoram Singer: Protein Family Classification Using Sparse Markov Transducers. ISMB 2000: 134-145

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

William W. Cohen, Robert E. Schapire, Yoram Singer: Learning to Order Things. J. Artif. Intell. Res. (JAIR) 10: 243-270 (1999)

Fernando C. N. Pereira, Yoram Singer: An Efficient Extension to Mixture Techniques for Prediction and Decision Trees. Machine Learning 36(3): 183-199 (1999)

Robert E. Schapire, Yoram Singer: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning 37(3): 297-336 (1999)

William W. Cohen, Yoram Singer: Context-Sensitive Learning Methods for Text Categorization. ACM Trans. Inf. Syst. 17(2): 141-173 (1999)

William W. Cohen, Yoram Singer: A Simple, Fast, and Effictive Rule Learner. AAAI/IAAI 1999: 335-342

Yoram Singer: Leveraged Vector Machines. NIPS 1999: 610-616

Dana Ron, Yoram Singer, Naftali Tishby: On the Learnability and Usage of Acyclic Probabilistic Finite Automata. J. Comput. Syst. Sci. 56(2): 133-152 (1998)

Shai Fine, Yoram Singer, Naftali Tishby: The Hierarchical Hidden Markov Model: Analysis and Applications. Machine Learning 32(1): 41-62 (1998)

Robert E. Schapire, Yoram Singer: Improved Boosting Algorithms using Confidence-Rated Predictions. COLT 1998: 80-91

Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer: An Efficient Boosting Algorithm for Combining Preferences. ICML 1998: 170-178

Nir Friedman, Yoram Singer: Efficient Bayesian Parameter Estimation in Large Discrete Domains. NIPS 1998: 417-423

Yoram Singer, Manfred K. Warmuth: Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy. NIPS 1998: 578-584

Robert E. Schapire, Yoram Singer, Amit Singhal: Boosting and Rocchio Applied to Text Filtering. SIGIR 1998: 215-223

Yoram Singer: Switching Portfolios. UAI 1998: 488-495

Yoram Singer: Switching Portfolios. Int. J. Neural Syst. 8(4): 445-455 (1997)

David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Machine Learning 27(1): 97-119 (1997)

Yoram Singer: Adaptive Mixtures of Probabilistic Transducers. Neural Computation 9(8): 1711-1733 (1997)

Fernando C. N. Pereira, Yoram Singer: An Efficient Extension to Mixture Techniques for Prediction and Decision Trees. COLT 1997: 114-121

Yoshua Bengio, Samy Bengio, Jean-Franc Isabelle, Yoram Singer: Shared Context Probabilistic Transducers. NIPS 1997

William W. Cohen, Robert E. Schapire, Yoram Singer: Learning to Order Things. NIPS 1997

Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: Using and Combining Predictors That Specialize. STOC 1997: 334-343

Eric Bauer, Daphne Koller, Yoram Singer: Update Rules for Parameter Estimation in Bayesian Networks. UAI 1997: 3-13

Dana Ron, Yoram Singer, Naftali Tishby: The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length. Machine Learning 25(2-3): 117-149 (1996)

David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: On-Line Portfolio Selection Using Multiplicative Updates. ICML 1996: 243-251

Yoram Singer, Manfred K. Warmuth: Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions. NIPS 1996: 641-647

William W. Cohen, Yoram Singer: Context-sensitive Learning Methods for Text Categorization. SIGIR 1996: 307-315

Fernando C. N. Pereira, Yoram Singer, Naftali Tishby: Beyond Word N-Grams. CoRR cmp-lg/9607016 (1996)

Dana Ron, Yoram Singer, Naftali Tishby: On the Learnability and Usage of Acyclic Probabilistic Finite Automata. COLT 1995: 31-40

David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth: A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT 1995: 69-78

Yoram Singer: Adaptive Mixture of Probabilistic Transducers. NIPS 1995: 381-387

Yoram Singer, Naftali Tishby: Dynamical encoding of cursive handwriting. Biological Cybernetics 71(3): 227-237 (1994)

Hinrich Schütze, Yoram Singer: Part-of-Speech Tagging using a Variable Memory Markov Model. ACL 1994: 181-187

Dana Ron, Yoram Singer, Naftali Tishby: Learning Probabilistic Automata with Variable Memory Length. COLT 1994: 35-46

Dana Ron, Yoram Singer, Naftali Tishby: The Power of Amnesia. NIPS 1993: 176-183

Yoram Singer, Naftali Tishby: Decoding Cursive Scripts. NIPS 1993: 833-840