Publication list

• Robert E. Schapire and Yoav Freund.
  Boosting: Foundations and Algorithms.
  MIT Press, 2012.
  Publisher's site.

• Alekh Agarwal, Miroslav Dudík, Satyen Kale, John Langford and Robert E. Schapire.
  Contextual bandit learning with predictable rewards.
  In Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012.
  Pdf.

• Cynthia Rudin, Robert E. Schapire and Ingrid Daubechies.
  Does AdaBoost always cycle? [open problem].
  In Proceedings of the 25th Annual Conference on Learning Theory, 2012.
  Pdf.

• Indraneel Mukherjee, Cynthia Rudin and Robert E. Schapire.
  The rate of convergence of AdaBoost.
  In The 24th Conference on Learning Theory, 2011.
  Pdf.

• Sina Jafarpour, Volkan Cevher and Robert E. Schapire.
  A game theoretic approach to expander-based compressive sensing.
  In Proceedings, IEEE International Symposium on Information Theory, 2011.
  Pdf.

• Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin and Robert E. Schapire.
  Contextual bandit algorithms with supervised learning guarantees.
  In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.
  Pdf.

• Wei Chu, Lihong Li, Lev Reyzin and Robert~E. Schapire.
  Contextual bandits with linear payoff functions.
  In Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011.
  Pdf.

• Sina Jafarpour, Robert E. Schapire and Volkan Cevher.
  Compressive sensing meets game theory.
  In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 2011.
  Pdf.

• Indraneel Mukherjee and Robert E. Schapire.
  A theory of multiclass boosting.
  In Advances in Neural Information Processing Systems 23, 2011.
  Pdf (conference paper).
  Pdf (supplement).

• Umar Syed and Robert E. Schapire.
  A reduction from apprenticeship learning to classification.
  In Advances in Neural Information Processing Systems 23, 2011.
  Pdf.

• Satyen Kale, Lev Reyzin and Robert E. Schapire.
  Non-stochastic bandit slate problems.
  In Advances in Neural Information Processing Systems 23, 2011.
  Pdf.

• Indraneel Mukherjee and Robert E. Schapire.
  Learning with continuous experts using drifting games.
  Theoretical Computer Science, 411:2670-2683, 2010.
  Pdf.

• Berk Kapicioglu, Robert E. Schapire, Martin Wikelski and Tamara Broderick.
  Combining spatial and telemetric features for learning animal movement models.
  In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, 2010.
  Pdf.

• Lihong Li, Wei Chu, John Langford and Robert E. Schapire.
  A contextual-bandit approach to personalized news article recommendation.
  In Proceedings of the 19th International Conference on World Wide Web, 2010.
  Pdf.

• Robert E. Schapire.
  The convergence rate of AdaBoost [open problem].
  In The 23rd Conference on Learning Theory, 2010.
  Pdf.

• Cynthia Rudin and Robert E. Schapire.
  Margin-based ranking and an equivalence between AdaBoost and RankBoost.
  Journal of Machine Learning Research 10:2193-2232, 2009.
  Pdf.

• Yongxin Taylor Xi, Zhen James Xiang, Peter J. Ramadge, Robert E. Schapire.
  Speed and sparsity of regularized boosting.
  In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009.
  Pdf.

• Zafer Barutcuoglu, Edoardo M. Airoldi, Vanessa Dumeaux, Robert E. Schapire, Olga G. Troyanskaya.
  Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields.
  Bioinformatics 25(10):1307-1313, 2009.
  Pdf.

• Ioannis Avramopoulos, Jennifer Rexford and Robert Schapire.
  From optimization to regret minimization and back again.
  In Proceedings of the Third Workshop on Tackling Computer System Problems with Machine Learning Techniques, 2008.
  Pdf.

• Umar Syed, Michael Bowling and Robert E. Schapire.
  Apprenticeship learning using linear programming.
  In Proceedings of the 25th International Conference on Machine Learning, 2008.
  Pdf.

• Yoav Freund and Robert E. Schapire.
  Response to Mease and Wyner, Evidence contrary to the statistical view of boosting, JMLR 9:131-156, 2008.
  Journal of Machine Learning Research, 9:171-174, 2008.
  Pdf.

• Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire and N.V. Vinodchandran.
  On reoptimizing multi-class classifiers.
  Machine Learning, 71:219-242, 2008.
  Pdf.

• Joseph K. Bradley and Robert E. Schapire.
  FilterBoost: Regression and classification on large datasets.
  In Advances in Neural Information Processing Systems 20, 2008.
  Pdf.

• Cynthia Rudin, Robert E. Schapire and Ingrid Daubechies.
  Analysis of boosting algorithms using the smooth margin function.
  The Annals of Statistics, 35(6):2723-2768, 2007.
  Pdf.

• Umar Syed and Robert E. Schapire.
  A game-theoretic approach to apprenticeship learning.
  In Advances in Neural Information Processing Systems 20, 2008.
  Pdf.

• Umar Syed and Robert E. Schapire.
  Imitation learning with a value-based prior.
  In Uncertainty in Artificial Intelligence: Proceedings of the Twenty-Third Conference, 2007.
  Pdf.

• Miroslav Dudík, David M. Blei and Robert E. Schapire.
  Hierarchical maximum entropy density estimation.
  In Proceedings of the 24th International Conference on Machine Learning, 2007.
  Pdf.

• Cynthia Rudin, Robert E. Schapire and Ingrid Daubechies.
  Precise statements of convergence for AdaBoost and arc-gv.
  AMS-IMS-SIAM Joint Summer Research Conference on Machine and Statistical Learning, Prediction and Discovery, pages 131-145, 2007.
  Pdf.

• Luis E. Ortiz, Robert E. Schapire and Sham M. Kakade.
  Maximum entropy correlated equilibria.
  In Eleventh International Conference on Artificial Intelligence and Statistics, 2007.
  Pdf.

• Miroslav Dudík, Steven J. Phillips and Robert E. Schapire.
  Maximum entropy density estimation with generalized regularization and an application to species distribution modeling.
  Journal of Machine Learning Research, 8(Jun):1217-1260, 2007.
  Pdf.

• Lev Reyzin and Robert E. Schapire.
  How boosting the margin can also boost classifier complexity.
  In Proceedings of the 23rd International Conference on Machine Learning, 2006.
  Pdf.

• Amit Agarwal, Elad Hazan, Satyen Kale and Robert E. Schapire.
  Algorithms for portfolio management based on the Newton method.
  In Proceedings of the 23rd International Conference on Machine Learning, 2006.
  Pdf.

• Miroslav Dudík and Robert E. Schapire.
  Maximum entropy distribution estimation with generalized regularization.
  In 19th Annual Conference on Learning Theory, 2006.
  Pdf.

• Jane Elith, Catherine H. Graham, Robert P. Anderson, Miroslav Dudík, Simon Ferrier, Antoine Guisan, Robert J. Hijmans, Falk Huettmann, John R. Leathwick, Anthony Lehmann, Jin Li, Lucia G. Lohmann, Bette A. Loiselle, Glenn Manion, Craig Moritz, Miguel Nakamura, Yoshinori Nakazawa, Jacob McC. M. Overton, A. Townsend Peterson, Steven J. Phillips, Karen Richardson, Ricardo Scachetti-Pereira, Robert E. Schapire, Jorge Soberón, Stephen Williams, Mary S. Wisz and Niklaus E. Zimmermann.
  Novel methods improve prediction of species' distributions from occurrence data.
  Ecography, 29:129-151, 2006.
  Pdf.

• Zafer Barutcuoglu, Robert E. Schapire and Olga G. Troyanskaya.
  Hierarchical multi-label prediction of gene function.
  Bioinformatics, 22:830-836, 2006.
  Pdf.

• Jordan Boyd-Graber, Christiane Fellbaum, Daniel Osherson and Robert Schapire.
  Adding dense, weighted connections to WordNet.
  In Proceedings of the Third International WordNet Conference, 2006.
  Pdf.

• Miroslav Dudík, Robert E. Schapire and Steven J. Phillips.
  Correcting sample selection bias in maximum entropy density estimation.
  In Advances in Neural Information Processing Systems 18, 2006.
  Pdf.

• Aurélie C. Lozano, Sanjeev R. Kulkarni and Robert E. Schapire.
  Convergence and consistency of regularized boosting algorithms with stationary beta-mixing observations.
  In Advances in Neural Information Processing Systems 18, 2006.
  Pdf.

• Cynthia Rudin, Corinna Cortes, Mehryar Mohri and Robert E. Schapire.
  Margin-based ranking meets boosting in the middle.
  In 18th Annual Conference on Computational Learning Theory, 2005.
  Pdf.

• Steven J. Phillips, Robert P. Anderson and Robert E. Schapire.
  Maximum entropy modeling of species geographic distributions.
  Ecological Modelling, 190:231-259, 2006.
  Pdf.

• Gokhan Tur, Dilek Hakkani-Tür and Robert E.Schapire.
  Combining active and semi-supervised learning for spoken language understanding.
  Speech Communication, 45(2):171-186, 2005.
  Pdf.

• Robert E. Schapire, Marie Rochery, Mazin Rahim and Narendra Gupta.
  Boosting with prior knowledge for call classification.
  IEEE Transactions on Speech and Audio Processing, 13(2), March, 2005.
  Pdf.

• Cynthia Rudin, Ingrid Daubechies and Robert E. Schapire.
  The dynamics of AdaBoost: Cyclic behavior and convergence of margins.
  Journal of Machine Learning Research, 5: 1557-1595, 2004.
  Pdf.

• Cynthia Rudin, Robert E. Schapire and Ingrid Daubechies.
  Boosting based on a smooth margin.
  In 17th Annual Conference on Computational Learning Theory, 2004.
  Postscript or gzipped postscript.

• Steven J. Phillips, Miroslav Dudík and Robert E. Schapire.
  A maximum entropy approach to species distribution modeling.
  In Proceedings of the Twenty-First International Conference on Machine Learning, pages 655-662, 2004.
  Pdf.

• Miroslav Dudík, Steven J. Phillips and Robert E. Schapire.
  Performance guarantees for regularized maximum entropy density estimation.
  In 17th Annual Conference on Learning Theory, 2004.
  Postscript or gzipped postscript.

• Cynthia Rudin, Ingrid Daubechies and Robert E. Schapire.
  On the dynamics of boosting.
  In Advances in Neural Information Processing Systems 16, 2004.
  Postscript or gzipped postscript.

• Yoav Freund, Yishay Mansour and Robert E. Schapire.
  Generalization bounds for averaged classifiers.
  The Annals of Statistics, 32(4):1698-1722, 2004.
  Pdf.

• Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik and David McAllester.
  Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions.
  Journal of Artificial Intelligence Research, 19:209-242, 2003.
  Postscript or gzipped postscript.

• Gokhan Tur, Robert E. Schapire and Dilek Hakkani-Tür.
  Active learning for spoken language understanding.
  In IEEE International Conference on Acoustics, Speech and Signal Processing, 2003.
  Pdf.

• Yoav Freund and Robert E. Schapire.
  A discussion of ``Process consistency for AdaBoost'' by Wenxin Jiang, ``On the Bayes-risk consistency of regularized boosting methods'' by Gábor Lugosi and Nicolas Vayatis, ``Statistical behavior and consistency of classification methods based on convex risk minimization'' by Tong Zhang.
  The Annals of Statistics, 32(1), 2004.
  Postscript or gzipped postscript.

• Robert E. Schapire.
  Advances in boosting.
  In Uncertainty in Artificial Intelligence: Proceedings of the Eighteenth Conference, 2002.
  Postscript or gzipped postscript.

• Giuseppe Di Fabbrizio, Dawn Dutton, Narendra Gupta, Barbara Hollister, Mazin Rahim, Giuseppe Riccardi, Robert Schapire and Juergen Schroeter.
  AT&T help desk.
  In 7th International Conference on Spoken Language Processing, 2002.
  Pdf.

• Robert E. Schapire, Peter Stone, David McAllester, Michael L. Littman and János A. Csirik.
  Modeling auction price uncertainty using boosting-based conditional density estimation.
  In Machine Learning: Proceedings of the Nineteenth International Conference, 2002.
  Postscript or gzipped postscript.

• Robert E. Schapire, Marie Rochery, Mazin Rahim and Narendra Gupta.
  Incorporating prior knowledge into boosting.
  In Machine Learning: Proceedings of the Nineteenth International Conference, 2002.
  Postscript or gzipped postscript.

• Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman and David McAllester.
  ATTac-2001: A learning, autonomous bidding agent.
  In Agent Mediated Electronic Commerce IV: Designing Mechanisms and Systems. Springer Verlag, 2002.
  Postscript or gzipped postscript.

• Robert E. Schapire.
  The boosting approach to machine learning: An overview.
  In D. D. Denison, M. H. Hansen, C. Holmes, B. Mallick, B. Yu, editors, Nonlinear Estimation and Classification. Springer, 2003.
  Postscript or gzipped postscript.

• M. Rochery, R. Schapire, M. Rahim, N. Gupta, G. Riccardi, S. Bangalore, H. Alshawi and S. Douglas.
  Combining prior knowledge and boosting for call classification in spoken language dialogue.
  In International Conference on Accoustics, Speech and Signal Processing, 2002.
  Postscript or gzipped postscript.

• Marie Rochery, Robert Schapire, Mazin Rahim and Narendra Gupta.
  BoosTexter for text categorization in spoken language dialogue.
  Accepted to Automatic Speech Recognition and Understanding Workshop, 2001 (but withdrawn due to travel restrictions following September 11).
  Postscript or gzipped postscript.

• Michael Collins, Sanjoy Dasgupta and Robert E. Schapire.
  A generalization of principal component analysis to the exponential family.
  In Advances in Neural Information Processing Systems 14, 2002.
  Postscript or gzipped postscript.

• Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire.
  The non-stochastic multi-armed bandit problem.
  SIAM Journal on Computing, 32(1):48-77, 2002.
  Postscript or gzipped postscript.

• David McAllester and Robert E. Schapire.
  Learning theory and language modeling.
  In Gerhard Lakemeyer and Bernhard Nebel, editors, Exploring Artificial Intelligence in the New Millenium. Morgan Kaufmann, 2002.
  Postscript or gzipped postscript.

• Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer and Amit Singhal.
  Boosting for document routing.
  In Proceedings of the Ninth International Conference on Information and Knowledge Management, 2000.
  Postscript or gzipped postscript.

• Michael Collins, Robert E. Schapire and Yoram Singer.
  Logistic regression, AdaBoost and Bregman distances.
  Machine Learning, 48(1/2/3), 2002.
  Postscript or gzipped postscript.

• David McAllester and Robert E. Schapire.
  On the convergence rate of Good-Turing estimators.
  Preliminary version appeared in Proceedings of the Thirteenth Annual Conference on Computational Learning Theory, 2000.
  Postscript or gzipped postscript of journal submission (5/17/01).

• Erin L. Allwein, Robert E. Schapire and Yoram Singer.
  Reducing multiclass to binary: A unifying approach for margin classifiers.
  Journal of Machine Learning Research, 1:113-141, 2000.
  Pdf.

• Yoav Freund, Yishay Mansour and Robert E. Schapire.
  Why averaging classifiers can protect against overfitting.
  Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001.

• Yoav Freund and Robert E. Schapire.
  Discussion of the paper "Additive logistic regression: a statistical view of boosting" by Jerome Friedman, Trevor Hastie and Robert Tibshirani.
  The Annals of Statistics, 28(2):391-393, April, 2000.
  Postscript or gzipped postscript.

• Robert E. Schapire.
  Theoretical views of boosting and applications.
  In Tenth International Conference on Algorithmic Learning Theory, 1999.
  Postscript or gzipped postscript.

• Yoav Freund and Robert E. Schapire.
  A short introduction to boosting.
  Journal of Japanese Society for Artificial Intelligence, 14(5):771-780, September, 1999. (Appearing in Japanese, translation by Naoki Abe.)
  Postscript or gzipped postscript.

• Robert E. Schapire.
  A brief introduction to boosting.
  In Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, 1999.
  Postscript or gzipped postscript.

• Robert E. Schapire.
  Drifting games.
  Machine Learning, 43(3):265-291, 2001.
  Postscript or gzipped postscript.

• Steven Abney and Robert E. Schapire and Yoram Singer.
  Boosting applied to tagging and PP attachment.
  In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, 1999.
  Postscript or gzipped postscript.

• Robert E. Schapire.
  Theoretical views of boosting.
  In Computational Learning Theory: Fourth European Conference, EuroCOLT'99, pages 1-10, 1999.
  Postscript or gzipped postscript.

• Yoav Freund, Raj Iyer, Robert E. Schapire and Yoram Singer.
  An efficient boosting algorithm for combining preferences.
  Journal of Machine Learning Research, 4:933-969, 2003.
  Postscript or compressed postscript.

• Robert E. Schapire and Yoram Singer.
  BoosTexter: A boosting-based system for text categorization.
  Machine Learning, 39(2/3):135-168, 2000.
  Postscript or compressed postscript.

• Yoav Freund and Robert E. Schapire.
  Large margin classification using the perceptron algorithm.
  Machine Learning, 37(3):277-296, 1999.
  Postscript or compressed postscript.

• Robert E. Schapire and Yoram Singer.
  Improved boosting algorithms using confidence-rated predictions.
  Machine Learning, 37(3):297-336, 1999.
  Postscript or compressed postscript.

• Robert E. Schapire, Yoram Singer and Amit Singhal.
  Boosting and Rocchio applied to text filtering.
  In SIGIR '98: Proceedings of the 21st Annual International Conference on Research and Development in Information Retrieval, pages 215-223, 1998.
  Postscript or compressed postscript.

• Yoav Freund and Robert E. Schapire.
  Adaptive game playing using multiplicative weights.
  Games and Economic Behavior, 29:79-103, 1999.
  Pdf.

• William W. Cohen, Robert E. Schapire and Yoram Singer.
  Learning to order things.
  Journal of Artificial Intelligence Research, 10:243-270, 1999.
  Postscript or compressed postscript.

• Robert E. Schapire, Yoav Freund, Peter Bartlett and Wee Sun Lee.
  Boosting the margin: A new explanation for the effectiveness of voting methods.
  The Annals of Statistics, 26(5):1651-1686, 1998.
  Postscript or compressed postscript.

• Yoav Freund and Robert E. Schapire.
  Discussion of the paper "Arcing Classifiers" by Leo Breiman.
  The Annals of Statistics, 26(3):824-832, 1998.
  Postscript or compressed postscript.

• Robert E. Schapire.
  Using output codes to boost multiclass learning problems.
  In Machine Learning: Proceedings of the Fourteenth International Conference, pages 313-321, 1997.
  Postscript or compressed postscript.

• Yoav Freund, Robert E. Schapire, Yoram Singer and Manfred K. Warmuth.
  Using and combining predictors that specialize.
  In Proceedings of the Twenty-Ninth Annual ACM Symposium on the Theory of Computing, pages 334--343, 1997.
  Postscript or compressed postscript.

• Yoav Freund and Robert E. Schapire.
  Experiments with a new boosting algorithm.
  In Machine Learning: Proceedings of the Thirteenth International Conference, pages 148-156, 1996.
  Postscript or compressed postscript.

• Yoav Freund and Robert E. Schapire.
  Game theory, on-line prediction and boosting.
  In Proceedings of the Ninth Annual Conference on Computational Learning Theory, pages 325-332, 1996.
  Pdf.

• David D. Lewis, Robert E. Schapire, James P. Callan, and Ron Papka.
  Training algorithms for linear text classifiers.
  In SIGIR '96: Proceedings of the 19th Annual International Conference on Research and Development in Information Retrieval, 1996.
  Postscript or compressed postscript.

• David P. Helmbold, Robert E. Schapire, Yoram Singer, and Manfred K. Warmuth.
  On-line portfolio selection using multiplicative updates.
  Mathematical Finance, 8(4):325-347, 1998.
  Postscript or compressed postscript.

• Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire.
  Gambling in a rigged casino: The adversarial multi-armed bandit problem.
  Extended abstract appeared in 36th Annual Symposium on Foundations of Computer Science, pages 322-331, 1995.
  Postscript or compressed postscript of significantly revised journal submission (6/8/98).

• Yoav Freund, Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, and Robert E. Schapire.
  Efficient algorithms for learning to play repeated games against computationally bounded adversaries.
  In 36th Annual Symposium on Foundations of Computer Science, pages 332-341, 1995.
  Postscript or compressed postscript.

• David P. Helmbold and Robert E. Schapire.
  Predicting nearly as well as the best pruning of a decision tree.
  Machine Learning, 27(1):51-68, 1997.
  Postscript or compressed postscript.

• David P. Helmbold, Robert E. Schapire, Yoram Singer, and Manfred K. Warmuth.
  A comparison of new and old algorithms for a mixture estimation problem.
  Machine Learning, 27(1):97-119, 1997.
  Postscript or compressed postscript.

• Yoav Freund and Robert E. Schapire.
  A decision-theoretic generalization of on-line learning and an application to boosting.
  Journal of Computer and System Sciences, 55(1):119-139, 1997.
  Postscript or compressed postscript.

• Robert E. Schapire and Manfred K. Warmuth.
  On the worst-case analysis of temporal-difference learning algorithms.
  Machine Learning, 22(1/2/3):95-121, 1996.
  Postscript or compressed postscript.

• Michael Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, and Linda Sellie.
  On the learnability of discrete distributions.
  In Proceedings of the Twenty-Sixth Annual ACM Symposium on the Theory of Computing, pages 273-282, 1994.
  Postscript or compressed postscript.

• Robert E. Schapire and Linda M. Sellie.
  Learning sparse multivariate polynomials over a field with queries and counterexamples.
  Journal of Computer and System Sciences, 52(2):201-213, April, 1996.
  Postscript or compressed postscript.

• Yoav Freund, Michael Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, and Linda Sellie.
  Efficient learning of typical finite automata from random walks.
  Information and Computation, 138(1):23-48, 1997.
  Pdf.

• Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, and Manfred K. Warmuth.
  How to use expert advice.
  Journal of the Association for Computing Machinery, 44(3):427-485, 1997.
  Postscript or compressed postscript.

• Harris Drucker, Robert Schapire, and Patrice Simard.
  Boosting performance in neural networks.
  International Journal of Pattern Recognition and Artificial Intelligence, 7(4):705-719, 1993.

• Harris Drucker, Robert Schapire, and Patrice Simard.
  Improving performance in neural networks using a boosting algorithm.
  In Advances in Neural Information Processing Systems 5, pages 42-49. Morgan Kaufmann, 1993.

• Michael J. Kearns, Robert E. Schapire, and Linda M. Sellie.
  Toward efficient agnostic learning.
  Machine Learning, 17:115-141, 1994.
  Pdf.

• David Haussler, Michael Kearns, Manfred Opper, and Robert Schapire.
  Estimating average-case learning curves using Bayesian, statistical physics and VC dimension methods.
  In Advances in Neural Information Processing Systems 4, pages 855-862. Morgan Kaufmann, 1992.

• Robert E. Schapire.
  Learning probabilistic read-once formulas on product distributions.
  Machine Learning, 14(1):47-81, 1994.
  Pdf.

• David Haussler, Michael Kearns, and Robert E. Schapire.
  Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension.
  Machine Learning, 14:83-113, 1994.
  Pdf.

• Robert E. Schapire.
  The Design and Analysis of Efficient Learning Algorithms.
  MIT Press, 1992.

• Sally A. Goldman, Michael J. Kearns, and Robert E. Schapire.
  Exact identification of read-once formulas using fixed points of amplification functions.
  SIAM Journal on Computing, 22(4):705-726, August 1993.

• Michael J. Kearns and Robert E. Schapire.
  Efficient distribution-free learning of probabilistic concepts.
  Journal of Computer and System Sciences, 48(3):464-497, 1994.
  Pdf.

• Sally A. Goldman, Michael J. Kearns, and Robert E. Schapire.
  On the sample complexity of weak learning.
  Information and Computation, 117(2):276-287, March 1995.
  Pdf.

• Robert E. Schapire.
  The emerging theory of average-case complexity.
  Technical Report Technical memo MIT/LCS/TM-431, MIT Laboratory for Computer Science, June 1990.

• Sally A. Goldman, Ronald L. Rivest, and Robert E. Schapire.
  Learning binary relations and total orders.
  SIAM Journal on Computing, 22(5):1006-1034, 1993.

• Robert E. Schapire.
  The strength of weak learnability.
  Machine Learning, 5(2):197-227, 1990.
  Pdf.

• Robert E. Schapire.
  Pattern languages are not learnable.
  In Proceedings of the Third Annual Workshop on Computational Learning Theory, pages 122-129, August 1990.

• Ronald L. Rivest and Robert E. Schapire.
  Diversity-based inference of finite automata.
  Journal of the Association for Computing Machinery, 41(3):555-589, May 1994.
  Pdf.

• Ronald L. Rivest and Robert E. Schapire.
  Inference of finite automata using homing sequences.
  Information and Computation, 103(2):299-347, April 1993.
  Pdf.

• Ronald L. Rivest and Robert E. Schapire.
  A new approach to unsupervised learning in deterministic environments.
  In Yves Kodratoff and Ryszard Michalski, editors, Machine Learning: An Artificial Intelligence Approach, volume III, pages 670-684. Morgan Kaufmann, 1990.

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