| 2008 |
|---|
| 100 | | John C. Platt,
Daphne Koller,
Yoram Singer,
Sam T. Roweis:
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
MIT Press 2008 |
| 99 | EE | Shai Shalev-Shwartz,
Yoram Singer:
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms.
COLT 2008: 311-322 |
| 98 | EE | John Duchi,
Shai Shalev-Shwartz,
Yoram Singer,
Tushar Chandra:
Efficient projections onto the l1-ball for learning in high dimensions.
ICML 2008: 272-279 |
| 97 | EE | Ofer Dekel,
Shai Shalev-Shwartz,
Yoram Singer:
The Forgetron: A Kernel-Based Perceptron on a Budget.
SIAM J. Comput. 37(5): 1342-1372 (2008) |
| 2007 |
|---|
| 96 | EE | 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 |
| 95 | EE | Shai Shalev-Shwartz,
Yoram Singer,
Nathan Srebro:
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM.
ICML 2007: 807-814 |
| 94 | EE | Joseph Keshet,
Shai Shalev-Shwartz,
Yoram Singer,
D. 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) |
| 93 | EE | Shai Shalev-Shwartz,
Yoram Singer:
A primal-dual perspective of online learning algorithms.
Machine Learning 69(2-3): 115-142 (2007) |
| 2006 |
|---|
| 92 | EE | Shai Shalev-Shwartz,
Yoram Singer:
Online Learning Meets Optimization in the Dual.
COLT 2006: 423-437 |
| 91 | EE | Ofer Dekel,
Philip M. Long,
Yoram Singer:
Online Multitask Learning.
COLT 2006: 453-467 |
| 90 | EE | Michael Fink,
Shai Shalev-Shwartz,
Yoram Singer,
Shimon Ullman:
Online multiclass learning by interclass hypothesis sharing.
ICML 2006: 313-320 |
| 89 | EE | Shai Shalev-Shwartz,
Yoram Singer:
Convex Repeated Games and Fenchel Duality.
NIPS 2006: 1265-1272 |
| 88 | EE | Yonatan Amit,
Shai Shalev-Shwartz,
Yoram Singer:
Online Classification for Complex Problems Using Simultaneous Projections.
NIPS 2006: 17-24 |
| 87 | EE | Ofer Dekel,
Yoram Singer:
Support Vector Machines on a Budget.
NIPS 2006: 345-352 |
| 86 | EE | Andrea Frome,
Yoram Singer,
Jitendra Malik:
Image Retrieval and Classification Using Local Distance Functions.
NIPS 2006: 417-424 |
| 85 | EE | Shai Shalev-Shwartz,
Yoram Singer:
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra.
Journal of Machine Learning Research 7: 1567-1599 (2006) |
| 84 | EE | 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 |
|---|
| 83 | EE | Shai Shalev-Shwartz,
Yoram Singer:
A New Perspective on an Old Perceptron Algorithm.
COLT 2005: 264-278 |
| 82 | EE | Koby Crammer,
Yoram Singer:
Loss Bounds for Online Category Ranking.
COLT 2005: 48-62 |
| 81 | EE | Ofer Dekel,
Yoram Singer:
Data-Driven Online to Batch Conversions.
NIPS 2005 |
| 80 | EE | Ofer Dekel,
Shai Shalev-Shwartz,
Yoram Singer:
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget.
NIPS 2005 |
| 79 | EE | Ofer Dekel,
Shai Shalev-Shwartz,
Yoram Singer:
Smooth epsiloon-Insensitive Regression by Loss Symmetrization.
Journal of Machine Learning Research 6: 711-741 (2005) |
| 78 | EE | Koby Crammer,
Yoram Singer:
Online Ranking by Projecting.
Neural Computation 17(1): 145-175 (2005) |
| 77 | EE | 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) |
| 2004 |
|---|
| 76 | | John Shawe-Taylor,
Yoram Singer:
Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings
Springer 2004 |
| 75 | EE | Ofer Dekel,
Joseph Keshet,
Yoram Singer:
Large margin hierarchical classification.
ICML 2004 |
| 74 | EE | Nir Krause,
Yoram Singer:
Leveraging the margin more carefully.
ICML 2004 |
| 73 | EE | Shai Shalev-Shwartz,
Yoram Singer,
Andrew Y. Ng:
Online and batch learning of pseudo-metrics.
ICML 2004 |
| 72 | EE | Shai Shalev-Shwartz,
Joseph Keshet,
Yoram Singer:
Learning to Align Polyphonic Music.
ISMIR 2004 |
| 71 | EE | Ofer Dekel,
Joseph Keshet,
Yoram Singer:
An Online Algorithm for Hierarchical Phoneme Classification.
MLMI 2004: 146-158 |
| 70 | EE | Lavi Shpigelman,
Koby Crammer,
Rony Paz,
Eilon Vaadia,
Yoram Singer:
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities.
NIPS 2004 |
| 69 | EE | Ofer Dekel,
Shai Shalev-Shwartz,
Yoram Singer:
The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees.
NIPS 2004 |
| 2003 |
|---|
| 68 | EE | Koby Crammer,
Yoram Singer:
Learning Algorithm for Enclosing Points in Bregmanian Spheres.
COLT 2003: 388-402 |
| 67 | EE | Ofer Dekel,
Shai Shalev-Shwartz,
Yoram Singer:
Smooth e-Intensive Regression by Loss Symmetrization.
COLT 2003: 433-447 |
| 66 | EE | Kristina Toutanova,
Dan Klein,
Christopher D. Manning,
Yoram Singer:
Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network.
HLT-NAACL 2003 |
| 65 | EE | Ofer Dekel,
Christopher D. Manning,
Yoram Singer:
Log-Linear Models for Label Ranking.
NIPS 2003 |
| 64 | EE | Koby Crammer,
Jaz S. Kandola,
Yoram Singer:
Online Classification on a Budget.
NIPS 2003 |
| 63 | EE | Shai Shalev-Shwartz,
Koby Crammer,
Ofer Dekel,
Yoram Singer:
Online Passive-Aggressive Algorithms.
NIPS 2003 |
| 62 | | Eleazar Eskin,
William Stafford Noble,
Yoram Singer:
Protein Family Classification Using Sparse Markov Transducers.
Journal of Computational Biology 10(2): 187-214 (2003) |
| 61 | EE | Koby Crammer,
Yoram Singer:
A Family of Additive Online Algorithms for Category Ranking.
Journal of Machine Learning Research 3: 1025-1058 (2003) |
| 60 | EE | Koby Crammer,
Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems.
Journal of Machine Learning Research 3: 951-991 (2003) |
| 59 | EE | 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) |
| 2002 |
|---|
| 58 | EE | Sanjoy Dasgupta,
Elan Pavlov,
Yoram Singer:
An Efficient PAC Algorithm for Reconstructing a Mixture of Lines.
ALT 2002: 351-364 |
| 57 | EE | Ehud Ben-Reuven,
Yoram Singer:
Discriminative Binaural Sound Localization.
NIPS 2002: 1229-1236 |
| 56 | EE | Lavi Shpigelman,
Yoram Singer,
Rony Paz,
Eilon Vaadia:
Spikernels: Embedding Spiking Neurons in Inner-Product Spaces.
NIPS 2002: 125-132 |
| 55 | EE | Koby Crammer,
Joseph Keshet,
Yoram Singer:
Kernel Design Using Boosting.
NIPS 2002: 537-544 |
| 54 | EE | Ofer Dekel,
Yoram Singer:
Multiclass Learning by Probabilistic Embeddings.
NIPS 2002: 945-952 |
| 53 | EE | Koby Crammer,
Yoram Singer:
A new family of online algorithms for category ranking.
SIGIR 2002: 151-158 |
| 52 | EE | Shai Shalev-Shwartz,
Shlomo Dubnov,
Nir Friedman,
Yoram Singer:
Robust temporal and spectral modeling for query By melody.
SIGIR 2002: 331-338 |
| 51 | | Eleazar Eskin,
William Stafford Noble,
Yoram Singer:
Using Substitution Matrices to Estimate Probability Distributions for Biological Sequences.
Journal of Computational Biology 9(6): 775-792 (2002) |
| 50 | | Koby Crammer,
Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems.
Machine Learning 47(2-3): 201-233 (2002) |
| 49 | | Michael Collins,
Robert E. Schapire,
Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances.
Machine Learning 48(1-3): 253-285 (2002) |
| 2001 |
|---|
| 48 | EE | Koby Crammer,
Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems.
COLT/EuroCOLT 2001: 99-115 |
| 47 | | 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 |
| 46 | EE | Koby Crammer,
Yoram Singer:
Pranking with Ranking.
NIPS 2001: 641-647 |
| 45 | EE | Koby Crammer,
Yoram Singer:
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines.
Journal of Machine Learning Research 2: 265-292 (2001) |
| 44 | | Yoram Singer:
Guest Editor's Introduction.
Machine Learning 43(3): 71-172 (2001) |
| 2000 |
|---|
| 43 | EE | Raj D. Iyer,
David D. Lewis,
Robert E. Schapire,
Yoram Singer,
Amit Singhal:
Boosting for Document Routing.
CIKM 2000: 70-77 |
| 42 | | Michael Collins,
Robert E. Schapire,
Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances.
COLT 2000: 158-169 |
| 41 | | Koby Crammer,
Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems.
COLT 2000: 35-46 |
| 40 | | Peter Ju,
Leslie Pack Kaelbling,
Yoram Singer:
State-based Classification of Finger Gestures from Electromyographic Signals.
ICML 2000: 439-446 |
| 39 | | Erin L. Allwein,
Robert E. Schapire,
Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers.
ICML 2000: 9-16 |
| 38 | | Eleazar Eskin,
William Noble Grundy,
Yoram Singer:
Protein Family Classification Using Sparse Markov Transducers.
ISMB 2000: 134-145 |
| 37 | | Koby Crammer,
Yoram Singer:
Improved Output Coding for Classification Using Continuous Relaxation.
NIPS 2000: 437-443 |
| 36 | EE | 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) |
| 35 | | Robert E. Schapire,
Yoram Singer:
BoosTexter: A Boosting-based System for Text Categorization.
Machine Learning 39(2/3): 135-168 (2000) |
| 1999 |
|---|
| 34 | | William W. Cohen,
Yoram Singer:
A Simple, Fast, and Effictive Rule Learner.
AAAI/IAAI 1999: 335-342 |
| 33 | EE | Yoram Singer:
Leveraged Vector Machines.
NIPS 1999: 610-616 |
| 32 | EE | William W. Cohen,
Yoram Singer:
Context-Sensitive Learning Methods for Text Categorization.
ACM Trans. Inf. Syst. 17(2): 141-173 (1999) |
| 31 | EE | William W. Cohen,
Robert E. Schapire,
Yoram Singer:
Learning to Order Things.
J. Artif. Intell. Res. (JAIR) 10: 243-270 (1999) |
| 30 | | Fernando C. N. Pereira,
Yoram Singer:
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees.
Machine Learning 36(3): 183-199 (1999) |
| 29 | | Robert E. Schapire,
Yoram Singer:
Improved Boosting Algorithms Using Confidence-rated Predictions.
Machine Learning 37(3): 297-336 (1999) |
| 1998 |
|---|
| 28 | EE | Robert E. Schapire,
Yoram Singer:
Improved Boosting Algorithms using Confidence-Rated Predictions.
COLT 1998: 80-91 |
| 27 | | Yoav Freund,
Raj D. Iyer,
Robert E. Schapire,
Yoram Singer:
An Efficient Boosting Algorithm for Combining Preferences.
ICML 1998: 170-178 |
| 26 | EE | Nir Friedman,
Yoram Singer:
Efficient Bayesian Parameter Estimation in Large Discrete Domains.
NIPS 1998: 417-423 |
| 25 | EE | Yoram Singer,
Manfred K. Warmuth:
Batch and On-Line Parameter Estimation of Gaussian Mixtures Based on the Joint Entropy.
NIPS 1998: 578-584 |
| 24 | EE | Robert E. Schapire,
Yoram Singer,
Amit Singhal:
Boosting and Rocchio Applied to Text Filtering.
SIGIR 1998: 215-223 |
| 23 | EE | Yoram Singer:
Switching Portfolios.
UAI 1998: 488-495 |
| 22 | | 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) |
| 21 | | Shai Fine,
Yoram Singer,
Naftali Tishby:
The Hierarchical Hidden Markov Model: Analysis and Applications.
Machine Learning 32(1): 41-62 (1998) |
| 1997 |
|---|
| 20 | EE | Fernando C. N. Pereira,
Yoram Singer:
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees.
COLT 1997: 114-121 |
| 19 | | William W. Cohen,
Robert E. Schapire,
Yoram Singer:
Learning to Order Things.
NIPS 1997 |
| 18 | | Yoshua Bengio,
Samy Bengio,
Jean-Franc Isabelle,
Yoram Singer:
Shared Context Probabilistic Transducers.
NIPS 1997 |
| 17 | EE | Yoav Freund,
Robert E. Schapire,
Yoram Singer,
Manfred K. Warmuth:
Using and Combining Predictors That Specialize.
STOC 1997: 334-343 |
| 16 | EE | Eric Bauer,
Daphne Koller,
Yoram Singer:
Update Rules for Parameter Estimation in Bayesian Networks.
UAI 1997: 3-13 |
| 15 | EE | Yoram Singer:
Switching Portfolios.
Int. J. Neural Syst. 8(4): 445-455 (1997) |
| 14 | | 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) |
| 13 | EE | Yoram Singer:
Adaptive Mixtures of Probabilistic Transducers.
Neural Computation 9(8): 1711-1733 (1997) |
| 1996 |
|---|
| 12 | | David P. Helmbold,
Robert E. Schapire,
Yoram Singer,
Manfred K. Warmuth:
On-Line Portfolio Selection Using Multiplicative Updates.
ICML 1996: 243-251 |
| 11 | EE | Yoram Singer,
Manfred K. Warmuth:
Training Algorithms for Hidden Markov Models using Entropy Based Distance Functions.
NIPS 1996: 641-647 |
| 10 | EE | William W. Cohen,
Yoram Singer:
Context-sensitive Learning Methods for Text Categorization.
SIGIR 1996: 307-315 |
| 9 | EE | Fernando C. N. Pereira,
Yoram Singer,
Naftali Tishby:
Beyond Word N-Grams
CoRR cmp-lg/9607016: (1996) |
| 8 | | 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) |
| 1995 |
|---|
| 7 | EE | Dana Ron,
Yoram Singer,
Naftali Tishby:
On the Learnability and Usage of Acyclic Probabilistic Finite Automata.
COLT 1995: 31-40 |
| 6 | EE | 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 |
| 5 | EE | Yoram Singer:
Adaptive Mixture of Probabilistic Transducers.
NIPS 1995: 381-387 |
| 1994 |
|---|
| 4 | | Hinrich Schütze,
Yoram Singer:
Part-of-Speech Tagging using a Variable Memory Markov Model.
ACL 1994: 181-187 |
| 3 | EE | Dana Ron,
Yoram Singer,
Naftali Tishby:
Learning Probabilistic Automata with Variable Memory Length.
COLT 1994: 35-46 |
| 1993 |
|---|
| 2 | EE | Dana Ron,
Yoram Singer,
Naftali Tishby:
The Power of Amnesia.
NIPS 1993: 176-183 |
| 1 | EE | Yoram Singer,
Naftali Tishby:
Decoding Cursive Scripts.
NIPS 1993: 833-840 |
