Ofer Dekel’s Publications


Online Learning with a Hint. Ofer Dekel, Arthur Flajolet, Nika Haghtalab, and Patrick Jaillet. To appear in Advances in Neural Information Processing Systems 30, 2017. OL

Adaptive Neural Networks for Efficient Inference. Tolga Bolukbasi, Joseph Wang, Ofer Dekel, and Venkatesh Saligrama. Proceedings of the Thirty Forth International Conference on Machine Learning, 2017. Bu

Linear Learning with Sparse Data. Ofer Dekel. Technical Report, 2016. AE

Bandit Smooth Convex Optimization: Improving the Bias-Variance Tradeoff. Ofer Dekel, Ronen Eldan, and Tomer Koren. Advances in Neural Information Processing Systems 28, 2015. Ba

Bandit Convex Optimization: sqrt(T) Regret in One Dimension. Sebastien Bubeck, Ofer Dekel, Tomer Koren, and Yuval Peres. Proceedings of the 28th Annual Conference on Learning Theory, 2015. Ba

Online Learning with Feedback Graphs: Beyond Bandits. Noga Alon, Nicolo Cesa-Bianchi, Ofer Dekel, and Tomer Koren. Proceedings of the 28th Annual Conference on Learning Theory, 2015. Ba

The Blinded Bandit: Learning with Adaptive Feedback. Ofer Dekel, Elad Hazan, and Tomer Koren. Advances in Neural Information Processing Systems 27, 2014. Ba

Online Learning with Composite Loss Functions. Ofer Dekel, Jian Ding, Tomer Koren, and Yuval Peres. Proceedings of the 27th Annual Conference on Learning Theory, 2014. AA Ba OL

Bandits with Switching Costs: T^(2/3) Regret. Ofer Dekel, Jian Ding, Tomer Koren, and Yuval Peres. Proceedings of the 46th Annual Symposium on the Theory of Computing, 2014. Ba LB

Online Learning with Switching Costs and Other Adaptive Adversaries. Nicolo Cesa-Bianchi, Ofer Dekel, and Ohad Shamir. Advances in Neural Information Processing Systems 26, pages 1160-1168, 2013. AA Ba LB OL

Better Rates for Any Adversarial Deterministic MDP. Ofer Dekel and Elad Hazan. Proceedings of the Thirtieth International Conference on Machine Learning, 2013. AA Ba MP

Selective Sampling and Active Learning from Single and Multiple Teachers. Ofer Dekel, Claudio Gentile, and Karthik Sridharan. Journal of Machine Learning Research, 13:2655-2697, 2012. AL MT

Optimal Distributed Online Prediction using Mini-Batches. Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao. Journal of Machine Learning Research, 13:165-202, 2012. DL OL

Deterministic MDPs with Adversarial Rewards and Bandit Feedback. Raman Arora, Ofer Dekel, and Ambuj Tewari. Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence, 2012. AA Ba MP

Online Bandit Learning Against an Adaptive Adversary: from Regret to Policy Regret. Raman Arora, Ofer Dekel, and Ambuj Tewari. Proceedings of the Twenty-Ninth International Conference on Machine Learning, 2012. AA Ba

There’s a Hole in My Dataspace: Piecewise Predictors for Heterogeneous Learning Problems. Ofer Dekel and Ohad Shamir. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings 22, 2012. EM

Optimal Distributed Online Prediction. Ofer Dekel, Ran Gilad-Bachrach, Ohad Shamir, and Lin Xiao. Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. DL OL

Bundle Selling by Online Estimation of Valuation Functions. Daniel Vainsencher, Ofer Dekel, and Shie Mannor. Proceedings of the Twenty-Eighth International Conference on Machine Learning, 2011. OL

Incentive Compatible Regression Learning. Ofer Dekel, Felix Fischer, and Ariel D. Procaccia. Journal of Computer and System Sciences, 76:759-777, 2010. MD LT

Learning to Classify with Missing and Corrupted Features. Ofer Dekel, Ohad Shamir, and Lin Xiao. Machine Learning Journal, 81:149-178, 2010.

Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback. Alekh Agarwal, Ofer Dekel, and Lin Xiao. Proceedings of the Twenty-Third Annual Conference on Learning Theory, pages 28-40, 2010. Ba

Robust Selective Sampling from Single and Multiple Teachers. Ofer Dekel, Claudio Gentile, and Karthik Sridharan. Proceedings of the Twenty-Third Annual Conference on Learning Theory, pages 346-358, 2010. AL MT

Multiclass-Multilabel Classification with More Classes than Examples. Ofer Dekel and Ohad Shamir. Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, JMLR 9:137-144, 2010. Ex

Distribution-Calibrated Hierarchical Classification. Ofer Dekel. Advances in Neural Information Processing Systems 22, 2010. Ex

Individual Sequence Prediction using Memory-Efficient Context Trees. Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer. IEEE Transactions on Information Theory, 55(11):5251-5262, 2009. OL

Vox Populi: Collecting High-Quality Labels from a Crowd. Ofer Dekel and Ohad Shamir. Proceedings of the Twenty-Second Annual Conference on Learning Theory, 2009. MT

Good Learners for Evil Teachers. Ofer Dekel and Ohad Shamir. Proceedings of the Twenty-Sixth International Conference on Machine Learning, pages 216-223, 2009. MT

From Online to Batch Learning with Cutoff Averaging. Ofer Dekel. Advances in Neural Information Processing Systems 21, 2009. LT

The Forgetron: A Kernel-Based Perceptron on a Budget. Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer. SIAM Journal on Computing, 37(5):1342-1372, 2008. Bu OL SV

Learning to Classify with Missing and Corrupted features. Ofer Dekel and Ohad Shamir. Proceedings of the Twenty-Fifth International Conference on Machine Learning, 2008.

Incentive Compatible Regression Learning. Ofer Dekel, Felix Fischer, and Ariel Procaccia. Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 884-893, 2008. MD

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

A Boosting Algorithm for Label Covering in Multilabel Problems. Yonatan Amit, Ofer Dekel, and Yoram Singer. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007. EM

Support Vector Machines on a Budget. Ofer Dekel and Yoram Singer. Advances in Neural Information Processing Systems 19, pages 345-352. MIT Press, 2007. Bu SV

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

Online Multitask Learning. Ofer Dekel, Philip M. Long, and Yoram Singer. Proceedings of the Nineteenth Annual Conference on Learning Theory, pages 453-467. Springer LNAI 4005, 2006. OL

Data-Driven Online to Batch Conversions. Ofer Dekel and Yoram Singer. Advances in Neural Information Processing Systems 18, pages 267-274. MIT Press, 2006. LT

The Forgetron: A Kernel-Based Perceptron on a Fixed Budget. Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer. Advances in Neural Information Processing Systems 18, pages 259-266. MIT Press, 2006. Bu OL SV

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

The Power of Selective Memory: Self-Bounded Learning of Prediction Suffix Trees. Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer. Advances in Neural Information Processing Systems 17, pages 345-352. MIT Press, 2005. OL

An Online Algorithm for Hierarchical Phoneme Classification. Ofer Dekel, Joseph Keshet, and Yoram Singer. Machine Learning for Multimodal Interaction: First International Workshop, pages 146-158. Springer LNAI 3361, 2005. OL

Large Margin Hierarchical Classification. Ofer Dekel, Joseph Keshet, and Yoram Singer. Proceedings of the Twenty-First International Conference on Machine Learning, 2004.

Log-Linear Models for Label Ranking. Ofer Dekel, Christopher Manning, and Yoram Singer. Advances in Neural Information Processing Systems 16, 2004.

Online Passive-Aggressive Algorithms. Koby Crammer, Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer. Advances in Neural Information Processing Systems 16. MIT Press, 2004. OL

Smooth Epsilon-Insensitive Regression by Loss Symmetrization. Ofer Dekel, Shai Shalev-Shwartz, and Yoram Singer. Proceedings of the Sixteenth Annual Conference on Computational Learning Theory, pages 433-447. Springer LNAI 2777, 2003.

Multiclass Categorization by Probabilistic Embeddings. Ofer Dekel and Yoram Singer. Advances in Neural Information Processing Systems 15, pages 945-952. MIT Press, 2003.


Tag Legend

AA Adaptive adversaries in online learning
AE Algorithm engineering and efficient implementation
AL Active learning
Ba Bandits
Bu Learning on a budget
DL Distrubuted learning
Ex Extreme classification (classification with many labels)
EM Boosting and ensemble methods
LB Lower bounds
LT Statistical learning theory
MD Mechanism design and incentives in machine learning
MP Markov decision processes
MT Learning from multiple teachers and crowdsourcing
OL Online learning
SV Support vector machines and kernel methods