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ORCIDYoram Singer
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- affiliation: Hebrew University of Jerusalem, Israel
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2020 – today
- 2020
[c99]Inbal Lavi, Shai Avidan, Yoram Singer, Yacov Hel-Or:
Proximity Preserving Binary Code Using Signed Graph-Cut. AAAI 2020: 4535-4544- [c98]Udaya Ghai, Elad Hazan, Yoram Singer:
Exponentiated Gradient Meets Gradient Descent. ALT 2020: 386-407 - [c97]Chiyuan Zhang, Samy Bengio, Moritz Hardt, Michael C. Mozer, Yoram Singer:
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization. ICLR 2020 - [i21]Inbal Lavi, Shai Avidan, Yoram Singer, Yacov Hel-Or:
Proximity Preserving Binary Code using Signed Graph-Cut. CoRR abs/2002.01793 (2020) - [i20]Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer:
Second Order Optimization Made Practical. CoRR abs/2002.09018 (2020)
2010 – 2019
- 2019
- [c96]Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory Efficient Adaptive Optimization. NeurIPS 2019: 9746-9755 - [i19]Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer:
Memory-Efficient Adaptive Optimization for Large-Scale Learning. CoRR abs/1901.11150 (2019) - [i18]Udaya Ghai, Elad Hazan, Yoram Singer:
Exponentiated Gradient Meets Gradient Descent. CoRR abs/1902.01903 (2019) - [i17]Chiyuan Zhang, Samy Bengio, Yoram Singer:
Are All Layers Created Equal? CoRR abs/1902.01996 (2019) - [i16]Chiyuan Zhang, Samy Bengio, Moritz Hardt, Yoram Singer:
Identity Crisis: Memorization and Generalization under Extreme Overparameterization. CoRR abs/1902.04698 (2019) - [i15]Michael L. Iuzzolino, Yoram Singer, Michael C. Mozer:
Convolutional Bipartite Attractor Networks. CoRR abs/1906.03504 (2019) - 2018
- [c95]Nishal P. Shah, Sasidhar Madugula, E. J. Chichilnisky, Yoram Singer, Jonathon Shlens:
Learning a neural response metric for retinal prosthesis. ICLR (Poster) 2018 - [c94]Vineet Gupta, Tomer Koren, Yoram Singer:
Shampoo: Preconditioned Stochastic Tensor Optimization. ICML 2018: 1837-1845 - [c93]Yuanzhi Li, Yoram Singer:
The Well-Tempered Lasso. ICML 2018: 3030-3038 - [i14]Vineet Gupta, Tomer Koren, Yoram Singer:
Shampoo: Preconditioned Stochastic Tensor Optimization. CoRR abs/1802.09568 (2018) - [i13]Yuanzhi Li, Yoram Singer:
The Well Tempered Lasso. CoRR abs/1806.03190 (2018) - 2017
- [c92]Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar:
Short and Deep: Sketching and Neural Networks. ICLR (Workshop) 2017 - [i12]Amit Daniely, Roy Frostig, Vineet Gupta, Yoram Singer:
Random Features for Compositional Kernels. CoRR abs/1703.07872 (2017) - [i11]Vineet Gupta, Tomer Koren, Yoram Singer:
A Unified Approach to Adaptive Regularization in Online and Stochastic Optimization. CoRR abs/1706.06569 (2017) - 2016
- [j39]Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer, Samy Bengio:
LLORMA: Local Low-Rank Matrix Approximation. J. Mach. Learn. Res. 17: 15:1-15:24 (2016) - [j38]Richard H. Byrd, S. L. Hansen, Jorge Nocedal, Yoram Singer:
A Stochastic Quasi-Newton Method for Large-Scale Optimization. SIAM J. Optim. 26(2): 1008-1031 (2016) - [c91]Moritz Hardt, Ben Recht, Yoram Singer:
Train faster, generalize better: Stability of stochastic gradient descent. ICML 2016: 1225-1234 - [c90]Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. NIPS 2016: 2253-2261 - [i10]Amit Daniely, Roy Frostig, Yoram Singer:
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity. CoRR abs/1602.05897 (2016) - [i9]Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar:
Sketching and Neural Networks. CoRR abs/1604.05753 (2016) - 2015
- [i8]Moritz Hardt, Benjamin Recht, Yoram Singer:
Train faster, generalize better: Stability of stochastic gradient descent. CoRR abs/1509.01240 (2015) - 2014
- [c89]Joonseok Lee, Samy Bengio, Seungyeon Kim, Guy Lebanon, Yoram Singer:
Local collaborative ranking. WWW 2014: 85-96 - [c88]Mohammad Norouzi, Tomás Mikolov, Samy Bengio, Yoram Singer, Jonathon Shlens, Andrea Frome, Greg Corrado, Jeffrey Dean:
Zero-Shot Learning by Convex Combination of Semantic Embeddings. ICLR 2014 - [i7]Richard H. Byrd, S. L. Hansen, Jorge Nocedal, Yoram Singer:
A Stochastic Quasi-Newton Method for Large-Scale Optimization. CoRR abs/1401.7020 (2014) - 2013
- [c87]Mark Stevens, Samy Bengio, Yoram Singer:
Efficient Learning of Sparse Ranking Functions. Empirical Inference 2013: 261-271 - [c86]Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer:
Local Low-Rank Matrix Approximation. ICML (2) 2013: 82-90 - [c85]Indraneel Mukherjee, Kevin Robert Canini, Rafael M. Frongillo
, Yoram Singer:
Parallel Boosting with Momentum. ECML/PKDD (3) 2013: 17-32 - [c84]Joonseok Lee, Seungyeon Kim, Guy Lebanon, Yoram Singer:
Matrix Approximation under Local Low-Rank Assumption. ICLR (Workshop Poster) 2013 - [i6]Yoram Singer:
Switching Portfolios. CoRR abs/1301.7413 (2013) - [i5]Eric Bauer, Daphne Koller, Yoram Singer:
Update Rules for Parameter Estimation in Bayesian Networks. CoRR abs/1302.1519 (2013) - [i4]Moshe Dubiner, Matan Gavish, Yoram Singer:
The Maximum Entropy Relaxation Path. CoRR abs/1311.1644 (2013) - [i3]Samy Bengio, Jeffrey Dean, Dumitru Erhan, Eugene Ie, Quoc V. Le, Andrew Rabinovich, Jonathon Shlens, Yoram Singer:
Using Web Co-occurrence Statistics for Improving Image Categorization. CoRR abs/1312.5697 (2013) - 2011
- [j37]John C. Duchi, Elad Hazan, Yoram Singer:
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. J. Mach. Learn. Res. 12: 2121-2159 (2011) - [j36]Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro, Andrew Cotter:
Pegasos: primal estimated sub-gradient solver for SVM. Math. Program. 127(1): 3-30 (2011) - [c83]Moshe Dubiner, Yoram Singer:
Entire Relaxation Path for Maximum Entropy Problems. EMNLP 2011: 941-948 - [i2]William W. Cohen, Robert E. Schapire, Yoram Singer:
Learning to Order Things. CoRR abs/1105.5464 (2011) - 2010
- [j35]Shai Shalev-Shwartz, Yoram Singer:
On the equivalence of weak learnability and linear separability: new relaxations and efficient boosting algorithms. Mach. Learn. 80(2-3): 141-163 (2010) - [c82]John C. Duchi, Shai Shalev-Shwartz, Yoram Singer, Ambuj Tewari:
Composite Objective Mirror Descent. COLT 2010: 14-26 - [c81]John C. Duchi, Elad Hazan, Yoram Singer:
Adaptive Subgradient Methods for Online Learning and Stochastic Optimization. COLT 2010: 257-269
2000 – 2009
- 2009
- [j34]John C. Duchi, Yoram Singer:
Efficient Online and Batch Learning Using Forward Backward Splitting. J. Mach. Learn. Res. 10: 2899-2934 (2009) - [j33]Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
Individual sequence prediction using memory-efficient context trees. IEEE Trans. Inf. Theory 55(11): 5251-5262 (2009) - [c80]John C. Duchi, Yoram Singer:
Boosting with structural sparsity. ICML 2009: 297-304 - [c79]Samy Bengio, Fernando C. N. Pereira, Yoram Singer, Dennis Strelow:
Group Sparse Coding. NIPS 2009: 82-89 - [c78]John C. Duchi, Yoram Singer:
Efficient Learning using Forward-Backward Splitting. NIPS 2009: 495-503 - 2008
- [j32]Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer:
Online Learning of Complex Prediction Problems Using Simultaneous Projections. J. Mach. Learn. Res. 9: 1399-1435 (2008) - [j31]Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
The Forgetron: A Kernel-Based Perceptron on a Budget. SIAM J. Comput. 37(5): 1342-1372 (2008) - [c77]Shai Shalev-Shwartz, Yoram Singer:
On the Equivalence of Weak Learnability and Linear Separability: New Relaxations and Efficient Boosting Algorithms. COLT 2008: 311-322 - [c76]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 - [e2]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. Curran Associates, Inc. 2008 [contents] - 2007
- [j30]Ofer Dekel, Philip M. Long, Yoram Singer:
Online Learning of Multiple Tasks with a Shared Loss. J. Mach. Learn. Res. 8: 2233-2264 (2007) - [j29]Shai Shalev-Shwartz, Yoram Singer:
A primal-dual perspective of online learning algorithms. Mach. Learn. 69(2-3): 115-142 (2007) - [j28]Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan:
A Large Margin Algorithm for Speech-to-Phoneme and Music-to-Score Alignment. IEEE Trans. Speech Audio Process. 15(8): 2373-2382 (2007) - [c75]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 - [c74]Shai Shalev-Shwartz, Yoram Singer, Nathan Srebro:
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. ICML 2007: 807-814 - [c73]Yonatan Amit, Ofer Dekel, Yoram Singer:
A Boosting Algorithm for Label Covering in Multilabel Problems. AISTATS 2007: 27-34 - [c72]Shai Shalev-Shwartz, Yoram Singer:
A Unified Algorithmic Approach for Efficient Online Label Ranking. AISTATS 2007: 452-459 - 2006
- [j27]Koby Crammer, Ofer Dekel, Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer:
Online Passive-Aggressive Algorithms. J. Mach. Learn. Res. 7: 551-585 (2006) - [j26]Shai Shalev-Shwartz, Yoram Singer:
Efficient Learning of Label Ranking by Soft Projections onto Polyhedra. J. Mach. Learn. Res. 7: 1567-1599 (2006) - [c71]Shai Shalev-Shwartz, Yoram Singer:
Online Learning Meets Optimization in the Dual. COLT 2006: 423-437 - [c70]Ofer Dekel, Philip M. Long, Yoram Singer:
Online Multitask Learning. COLT 2006: 453-467 - [c69]Michael Fink, Shai Shalev-Shwartz, Yoram Singer, Shimon Ullman:
Online multiclass learning by interclass hypothesis sharing. ICML 2006: 313-320 - [c68]Joseph Keshet, Shai Shalev-Shwartz, Samy Bengio, Yoram Singer, Dan Chazan:
Discriminative kernel-based phoneme sequence recognition. INTERSPEECH 2006 - [c67]Yonatan Amit, Shai Shalev-Shwartz, Yoram Singer:
Online Classification for Complex Problems Using Simultaneous Projections. NIPS 2006: 17-24 - [c66]Ofer Dekel, Yoram Singer:
Support Vector Machines on a Budget. NIPS 2006: 345-352 - [c65]Andrea Frome, Yoram Singer, Jitendra Malik:
Image Retrieval and Classification Using Local Distance Functions. NIPS 2006: 417-424 - [c64]Shai Shalev-Shwartz, Yoram Singer:
Convex Repeated Games and Fenchel Duality. NIPS 2006: 1265-1272 - 2005
- [j25]Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
Smooth epsiloon-Insensitive Regression by Loss Symmetrization. J. Mach. Learn. Res. 6: 711-741 (2005) - [j24]Koby Crammer, Yoram Singer:
Online Ranking by Projecting. Neural Comput. 17(1): 145-175 (2005) - [j23]Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia:
Spikernels: Predicting Arm Movements by Embedding Population Spike Rate Patterns in Inner-Product Spaces. Neural Comput. 17(3): 671-690 (2005) - [c63]Koby Crammer, Yoram Singer:
Loss Bounds for Online Category Ranking. COLT 2005: 48-62 - [c62]Shai Shalev-Shwartz, Yoram Singer:
A New Perspective on an Old Perceptron Algorithm. COLT 2005: 264-278 - [c61]Joseph Keshet, Shai Shalev-Shwartz, Yoram Singer, Dan Chazan:
Phoneme alignment based on discriminative learning. INTERSPEECH 2005: 2961-2964 - [c60]Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
The Forgetron: A Kernel-Based Perceptron on a Fixed Budget. NIPS 2005: 259-266 - [c59]Ofer Dekel, Yoram Singer:
Data-Driven Online to Batch Conversions. NIPS 2005: 267-274 - 2004
- [c58]Ofer Dekel, Joseph Keshet, Yoram Singer:
Large margin hierarchical classification. ICML 2004 - [c57]Nir Krause, Yoram Singer:
Leveraging the margin more carefully. ICML 2004 - [c56]Shai Shalev-Shwartz, Yoram Singer, Andrew Y. Ng:
Online and batch learning of pseudo-metrics. ICML 2004 - [c55]Shai Shalev-Shwartz, Joseph Keshet, Yoram Singer:
Learning to Align Polyphonic Music. ISMIR 2004 - [c54]Ofer Dekel, Joseph Keshet, Yoram Singer:
An Online Algorithm for Hierarchical Phoneme Classification. MLMI 2004: 146-158 - [c53]Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees. NIPS 2004: 345-352 - [c52]Lavi Shpigelman, Koby Crammer, Rony Paz, Eilon Vaadia, Yoram Singer:
A Temporal Kernel-Based Model for Tracking Hand Movements from Neural Activities. NIPS 2004: 1273-1280 - [e1]John Shawe-Taylor, Yoram Singer:
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 [contents] - 2003
- [j22]Eleazar Eskin, William Stafford Noble, Yoram Singer:
Protein Family Classification Using Sparse Markov Transducers. J. Comput. Biol. 10(2): 187-213 (2003) - [j21]Koby Crammer, Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems. J. Mach. Learn. Res. 3: 951-991 (2003) - [j20]Koby Crammer, Yoram Singer:
A Family of Additive Online Algorithms for Category Ranking. J. Mach. Learn. Res. 3: 1025-1058 (2003) - [j19]Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer:
An Efficient Boosting Algorithm for Combining Preferences. J. Mach. Learn. Res. 4: 933-969 (2003) - [c51]Koby Crammer, Yoram Singer:
Learning Algorithm for Enclosing Points in Bregmanian Spheres. COLT 2003: 388-402 - [c50]Ofer Dekel, Shai Shalev-Shwartz, Yoram Singer:
Smooth e-Intensive Regression by Loss Symmetrization. COLT 2003: 433-447 - [c49]Kristina Toutanova, Dan Klein, Christopher D. Manning, Yoram Singer:
Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network. HLT-NAACL 2003 - [c48]Koby Crammer, Jaz S. Kandola, Yoram Singer:
Online Classification on a Budget. NIPS 2003: 225-232 - [c47]Ofer Dekel, Christopher D. Manning, Yoram Singer:
Log-Linear Models for Label Ranking. NIPS 2003: 497-504 - [c46]Shai Shalev-Shwartz, Koby Crammer, Ofer Dekel, Yoram Singer:
Online Passive-Aggressive Algorithms. NIPS 2003: 1229-1236 - 2002
- [j18]Eleazar Eskin, William Stafford Noble, Yoram Singer:
Using Substitution Matrices to Estimate Probability Distributions for Biological Sequences. J. Comput. Biol. 9(6): 775-791 (2002) - [j17]Koby Crammer, Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems. Mach. Learn. 47(2-3): 201-233 (2002) - [j16]Michael Collins, Robert E. Schapire, Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances. Mach. Learn. 48(1-3): 253-285 (2002) - [c45]Sanjoy Dasgupta, Elan Pavlov, Yoram Singer:
An Efficient PAC Algorithm for Reconstructing a Mixture of Lines. ALT 2002: 351-364 - [c44]Lavi Shpigelman, Yoram Singer, Rony Paz, Eilon Vaadia:
Spikernels: Embedding Spiking Neurons in Inner-Product Spaces. NIPS 2002: 125-132 - [c43]Koby Crammer, Joseph Keshet, Yoram Singer:
Kernel Design Using Boosting. NIPS 2002: 537-544 - [c42]Ofer Dekel, Yoram Singer:
Multiclass Learning by Probabilistic Embeddings. NIPS 2002: 945-952 - [c41]Ehud Ben-Reuven, Yoram Singer:
Discriminative Binaural Sound Localization. NIPS 2002: 1229-1236 - [c40]Koby Crammer, Yoram Singer:
A new family of online algorithms for category ranking. SIGIR 2002: 151-158 - [c39]Shai Shalev-Shwartz, Shlomo Dubnov, Nir Friedman, Yoram Singer:
Robust temporal and spectral modeling for query By melody. SIGIR 2002: 331-338 - 2001
- [j15]Koby Crammer, Yoram Singer:
On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines. J. Mach. Learn. Res. 2: 265-292 (2001) - [j14]Yoram Singer:
Guest Editor's Introduction. Mach. Learn. 43(3): 71-172 (2001) - [c38]Koby Crammer, Yoram Singer:
Ultraconservative Online Algorithms for Multiclass Problems. COLT/EuroCOLT 2001: 99-115 - [c37]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 - [c36]Koby Crammer, Yoram Singer:
Pranking with Ranking. NIPS 2001: 641-647 - 2000
- [j13]Erin L. Allwein, Robert E. Schapire, Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. J. Mach. Learn. Res. 1: 113-141 (2000) - [j12]Robert E. Schapire, Yoram Singer:
BoosTexter: A Boosting-based System for Text Categorization. Mach. Learn. 39(2/3): 135-168 (2000) - [c35]Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal:
Boosting for Document Routing. CIKM 2000: 70-77 - [c34]Koby Crammer, Yoram Singer:
On the Learnability and Design of Output Codes for Multiclass Problems. COLT 2000: 35-46 - [c33]Michael Collins, Robert E. Schapire, Yoram Singer:
Logistic Regression, AdaBoost and Bregman Distances. COLT 2000: 158-169 - [c32]Erin L. Allwein, Robert E. Schapire, Yoram Singer:
Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. ICML 2000: 9-16 - [c31]Peter Ju, Leslie Pack Kaelbling, Yoram Singer:
State-based Classification of Finger Gestures from Electromyographic Signals. ICML 2000: 439-446 - [c30]Eleazar Eskin, William Noble Grundy, Yoram Singer:
Protein Family Classification Using Sparse Markov Transducers. ISMB 2000: 134-145 - [c29]Koby Crammer, Yoram Singer:
Improved Output Coding for Classification Using Continuous Relaxation. NIPS 2000: 437-443
1990 – 1999
- 1999
- [j11]William W. Cohen, Robert E. Schapire, Yoram Singer:
Learning to Order Things. J. Artif. Intell. Res. 10: 243-270 (1999) - [j10]Fernando C. N. Pereira, Yoram Singer:
An Efficient Extension to Mixture Techniques for Prediction and Decision Trees. Mach. Learn. 36(3): 183-199 (1999) - [j9]Robert E. Schapire, Yoram Singer:
Improved Boosting Algorithms Using Confidence-rated Predictions. Mach. Learn. 37(3): 297-336 (1999) - [j8]