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All publications by Robert E. Schapire
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Efficient and Parsimonious Agnostic Active Learning
Tzu-kuo Huang, Alekh Agarwal, Daniel J. Hsu, John Langford and Robert E. Schapire
Advances in Neural Information Processing Systems 28, 2015


Fast Convergence of Regularized Learning in Games
Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo and Robert E. Schapire
Advances in Neural Information Processing Systems 28, 2015


A Drifting-Games Analysis for Online Learning and Applications to Boosting
Haipeng Luo and Robert E. Schapire
Advances in Neural Information Processing Systems 27, 2014


Contextual Bandit Learning with Predictable Rewards
Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford and Robert E. Schapire
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics (AISTATS-12), 2012


Contextual Bandits with Linear Payoff Functions
Wei Chu, Lihong Li, Lev Reyzin and Robert E. Schapire
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


Contextual Bandit Algorithms with Supervised Learning Guarantees
Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin and Robert E. Schapire
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS-11), 2011


A Theory of Multiclass Boosting
Indraneel Mukherjee and Robert E. Schapire
Advances in Neural Information Processing Systems 23, 2010


Non-Stochastic Bandit Slate Problems
Satyen Kale, Lev Reyzin and Robert E. Schapire
Advances in Neural Information Processing Systems 23, 2010


A Reduction from Apprenticeship Learning to Classification
Umar Syed and Robert E. Schapire
Advances in Neural Information Processing Systems 23, 2010


Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
Cynthia Rudin and Robert E. Schapire
Journal of Machine Learning Research, 2009


Speed and Sparsity of Regularized Boosting
Yongxin T. Xi, Zhen J. Xiang, Peter J. Ramadge and Robert E. Schapire
Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS-09), 2009


Apprenticeship learning using linear programming
Umar Syed, Michael H. Bowling and Robert E. Schapire
Proceedings of the 25th International Conference on Machine Learning (ICML-08), 2008


Hierarchical maximum entropy density estimation
Miroslav Dudík, David M. Blei and Robert E. Schapire
Proceedings of the 24th International Conference on Machine Learning (ICML-07), 2007


A Game-Theoretic Approach to Apprenticeship Learning
Umar Syed and Robert E. Schapire
Advances in Neural Information Processing Systems 20, 2007


FilterBoost: Regression and Classification on Large Datasets
Joseph K. Bradley and Robert E. Schapire
Advances in Neural Information Processing Systems 20, 2007


Maximum Entropy Density Estimation with Generalized Regularization and an Application to Species Distribution Modeling
Miroslav Dudík, Steven J. Phillips and Robert E. Schapire
Journal of Machine Learning Research, 2007


Maximum Entropy Correlated Equilibria
Luis E. Ortiz, Robert E. Schapire and Sham M. Kakade
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), 2007


How boosting the margin can also boost classifier complexity
Lev Reyzin and Robert E. Schapire
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Algorithms for portfolio management based on the Newton method
Amit Agarwal, Elad Hazan, Satyen Kale and Robert E. Schapire
Proceedings of the 23th International Conference on Machine Learning (ICML-06), 2006


Correcting sample selection bias in maximum entropy density estimation
Miroslav Dudík, Steven J. Phillips and Robert E. Schapire
Advances in Neural Information Processing Systems 18, 2005


Convergence and Consistency of Regularized Boosting Algorithms with Stationary $\beta$-Mixing Observations
Sanjeev Kulkarni, Aurelie C. Lozano and Robert E. Schapire
Advances in Neural Information Processing Systems 18, 2005


A maximum entropy approach to species distribution modeling
Steven J. Phillips, Miroslav Dudík and Robert E. Schapire
Proceedings of the 21st International Conference on Machine Learning (ICML-04), 2004


The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
Cynthia Rudin, Ingrid Daubechies and Robert E. Schapire
Journal of Machine Learning Research, 2004


On the Dynamics of Boosting
Cynthia Rudin, Ingrid Daubechies and Robert E. Schapire
Advances in Neural Information Processing Systems 16, 2003


An Efficient Boosting Algorithm for Combining Preferences
Yoav Freund, Raj D. Iyer, Robert E. Schapire and Yoram Singer
Journal of Machine Learning Research, 2003


A Generalization of Principal Components Analysis to the Exponential Family
Michael Collins, S. Dasgupta and Robert E. Schapire
Advances in Neural Information Processing Systems 14, 2001


Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers
Erin L. Allwein, Robert E. Schapire and Yoram Singer
Journal of Machine Learning Research, 2000