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Robert E. Schapire: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Robert E. Schapire, Yoram Singer
    BoosTexter: A Boosting-based System for Text Categorization. [Citation Graph (1, 0)][DBLP]
    Machine Learning, 2000, v:39, n:2/3, pp:135-168 [Journal]
  2. Robert E. Schapire
    Theoretical Views of Boosting and Applications. [Citation Graph (0, 0)][DBLP]
    ATL, 1999, pp:13-25 [Conf]
  3. Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester
    ATTac-2001: A Learning, Autonomous Bidding Agent. [Citation Graph (0, 0)][DBLP]
    AMEC, 2002, pp:143-160 [Conf]
  4. Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal
    Boosting for Document Routing. [Citation Graph (0, 0)][DBLP]
    CIKM, 2000, pp:70-77 [Conf]
  5. Miroslav Dudík, Steven J. Phillips, Robert E. Schapire
    Performance Guarantees for Regularized Maximum Entropy Density Estimation. [Citation Graph (0, 0)][DBLP]
    COLT, 2004, pp:472-486 [Conf]
  6. Miroslav Dudík, Robert E. Schapire
    Maximum Entropy Distribution Estimation with Generalized Regularization. [Citation Graph (0, 0)][DBLP]
    COLT, 2006, pp:123-138 [Conf]
  7. Michael Collins, Robert E. Schapire, Yoram Singer
    Logistic Regression, AdaBoost and Bregman Distances. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:158-169 [Conf]
  8. Yoav Freund, Robert E. Schapire
    Game Theory, On-Line Prediction and Boosting. [Citation Graph (0, 0)][DBLP]
    COLT, 1996, pp:325-332 [Conf]
  9. Yoav Freund, Robert E. Schapire
    Large Margin Classification Using the Perceptron Algorithm. [Citation Graph (0, 0)][DBLP]
    COLT, 1998, pp:209-217 [Conf]
  10. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    On the Sample Complexity of Weak Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:217-231 [Conf]
  11. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:388- [Conf]
  12. David Haussler, Michael J. Kearns, Robert E. Schapire
    Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:61-74 [Conf]
  13. David P. Helmbold, Robert E. Schapire
    Predicting Nearly as Well as the Best Pruning of a Decision Tree. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:61-68 [Conf]
  14. David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth
    A Comparison of New and Old Algorithms for a Mixture Estimation Problem. [Citation Graph (0, 0)][DBLP]
    COLT, 1995, pp:69-78 [Conf]
  15. Michael J. Kearns, Robert E. Schapire
    Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:389- [Conf]
  16. Michael J. Kearns, Robert E. Schapire, Linda Sellie
    Toward Efficient Agnostic Learning. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:341-352 [Conf]
  17. David A. McAllester, Robert E. Schapire
    On the Convergence Rate of Good-Turing Estimators. [Citation Graph (0, 0)][DBLP]
    COLT, 2000, pp:1-6 [Conf]
  18. Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire
    Margin-Based Ranking Meets Boosting in the Middle. [Citation Graph (0, 0)][DBLP]
    COLT, 2005, pp:63-78 [Conf]
  19. Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies
    Boosting Based on a Smooth Margin. [Citation Graph (0, 0)][DBLP]
    COLT, 2004, pp:502-517 [Conf]
  20. Robert E. Schapire
    Pattern Languages are not Learnable. [Citation Graph (0, 0)][DBLP]
    COLT, 1990, pp:122-129 [Conf]
  21. Robert E. Schapire
    Learning Probabilistic Read-Once Formulas on Product Distributions. [Citation Graph (0, 0)][DBLP]
    COLT, 1991, pp:184-198 [Conf]
  22. Robert E. Schapire
    Drifting Games. [Citation Graph (0, 0)][DBLP]
    COLT, 1999, pp:114-124 [Conf]
  23. Robert E. Schapire, Linda Sellie
    Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. [Citation Graph (0, 0)][DBLP]
    COLT, 1993, pp:17-26 [Conf]
  24. Robert E. Schapire, Yoram Singer
    Improved Boosting Algorithms using Confidence-Rated Predictions. [Citation Graph (0, 0)][DBLP]
    COLT, 1998, pp:80-91 [Conf]
  25. Robert E. Schapire
    Theoretical Views of Boosting. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1999, pp:1-10 [Conf]
  26. Yoav Freund, Robert E. Schapire
    A decision-theoretic generalization of on-line learning and an application to boosting. [Citation Graph (0, 0)][DBLP]
    EuroCOLT, 1995, pp:23-37 [Conf]
  27. Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
    Gambling in a Rigged Casino: The Adversarial Multi-Arm Bandit Problem. [Citation Graph (0, 0)][DBLP]
    FOCS, 1995, pp:322-331 [Conf]
  28. Yoav Freund, Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire
    Efficient Algorithms for Learning to Play Repeated Games Against Computationally Bounded Adversaries. [Citation Graph (0, 0)][DBLP]
    FOCS, 1995, pp:332-341 [Conf]
  29. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1990, pp:193-202 [Conf]
  30. Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire
    Learning Binary Relations and Total Orders (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1989, pp:46-51 [Conf]
  31. Michael J. Kearns, Robert E. Schapire
    Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1990, pp:382-391 [Conf]
  32. Ronald L. Rivest, Robert E. Schapire
    Diversity-Based Inference of Finite Automata (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1987, pp:78-87 [Conf]
  33. Robert E. Schapire
    The Strength of Weak Learnability (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    FOCS, 1989, pp:28-33 [Conf]
  34. Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire
    Algorithms for portfolio management based on the Newton method. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:9-16 [Conf]
  35. Erin L. Allwein, Robert E. Schapire, Yoram Singer
    Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. [Citation Graph (0, 0)][DBLP]
    ICML, 2000, pp:9-16 [Conf]
  36. Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer
    An Efficient Boosting Algorithm for Combining Preferences. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:170-178 [Conf]
  37. Yoav Freund, Robert E. Schapire
    Experiments with a New Boosting Algorithm. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:148-156 [Conf]
  38. David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
    On-Line Portfolio Selection Using Multiplicative Updates. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:243-251 [Conf]
  39. Lev Reyzin, Robert E. Schapire
    How boosting the margin can also boost classifier complexity. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:753-760 [Conf]
  40. Steven J. Phillips, Miroslav Dudík, Robert E. Schapire
    A maximum entropy approach to species distribution modeling. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  41. Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik
    Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:546-553 [Conf]
  42. Robert E. Schapire
    Using output codes to boost multiclass learning problems. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:313-321 [Conf]
  43. Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee
    Boosting the margin: A new explanation for the effectiveness of voting methods. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:322-330 [Conf]
  44. Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra Gupta
    Incorporating Prior Knowledge into Boosting. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:538-545 [Conf]
  45. Robert E. Schapire, Manfred K. Warmuth
    On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:266-274 [Conf]
  46. Robert E. Schapire
    A Brief Introduction to Boosting. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1999, pp:1401-1406 [Conf]
  47. William W. Cohen, Robert E. Schapire, Yoram Singer
    Learning to Order Things. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  48. Michael Collins, S. DasGupta, Robert E. Schapire
    A Generalization of Principal Components Analysis to the Exponential Family. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:617-624 [Conf]
  49. Harris Drucker, Robert E. Schapire, Patrice Simard
    Improving Performance in Neural Networks Using a Boosting Algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 1992, pp:42-49 [Conf]
  50. Miroslav Dudík, Robert E. Schapire, Steven J. Phillips
    Correcting sample selection bias in maximum entropy density estimation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  51. David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire
    Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:855-862 [Conf]
  52. Aurelie C. Lozano, Sanjeev Kulkarni, Robert E. Schapire
    Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  53. Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire
    On the Dynamics of Boosting. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  54. Ronald L. Rivest, Robert E. Schapire
    Inference of Finite Automata Using Homing Sequences. [Citation Graph (0, 0)][DBLP]
    Machine Learning: From Theory to Applications, 1993, pp:51-73 [Conf]
  55. David D. Lewis, Robert E. Schapire, James P. Callan, Ron Papka
    Training Algorithms for Linear Text Classifiers. [Citation Graph (0, 0)][DBLP]
    SIGIR, 1996, pp:298-306 [Conf]
  56. Robert E. Schapire, Yoram Singer, Amit Singhal
    Boosting and Rocchio Applied to Text Filtering. [Citation Graph (0, 0)][DBLP]
    SIGIR, 1998, pp:215-223 [Conf]
  57. Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth
    How to use expert advice. [Citation Graph (0, 0)][DBLP]
    STOC, 1993, pp:382-391 [Conf]
  58. Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
    Efficient learning of typical finite automata from random walks. [Citation Graph (0, 0)][DBLP]
    STOC, 1993, pp:315-324 [Conf]
  59. Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
    Using and Combining Predictors That Specialize. [Citation Graph (0, 0)][DBLP]
    STOC, 1997, pp:334-343 [Conf]
  60. Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
    On the learnability of discrete distributions. [Citation Graph (0, 0)][DBLP]
    STOC, 1994, pp:273-282 [Conf]
  61. Ronald L. Rivest, Robert E. Schapire
    Inference of Finite Automata Using Homing Sequences (Extended Abstract) [Citation Graph (0, 0)][DBLP]
    STOC, 1989, pp:411-420 [Conf]
  62. Robert E. Schapire
    Advances in Boosting. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:446-452 [Conf]
  63. Zafer Barutçuoglu, Robert E. Schapire, Olga G. Troyanskaya
    Hierarchical multi-label prediction of gene function. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2006, v:22, n:7, pp:830-836 [Journal]
  64. Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
    Gambling in a rigged casino: The adversarial multi-armed bandit problem [Citation Graph (0, 0)][DBLP]
    Electronic Colloquium on Computational Complexity (ECCC), 2000, v:7, n:68, pp:- [Journal]
  65. Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie
    Efficient Learning of Typical Finite Automata from Random Walks. [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1997, v:138, n:1, pp:23-48 [Journal]
  66. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    On the Sample Complexity of Weakly Learning [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1995, v:117, n:2, pp:276-287 [Journal]
  67. Ronald L. Rivest, Robert E. Schapire
    Inference of Finite Automata Using Homing Sequences [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1993, v:103, n:2, pp:299-347 [Journal]
  68. Harris Drucker, Robert E. Schapire, Patrice Simard
    Boosting Performance in Neural Networks. [Citation Graph (0, 0)][DBLP]
    IJPRAI, 1993, v:7, n:4, pp:705-719 [Journal]
  69. Nicolò Cesa-Bianchi, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, Manfred K. Warmuth
    How to use expert advice. [Citation Graph (0, 0)][DBLP]
    J. ACM, 1997, v:44, n:3, pp:427-485 [Journal]
  70. Ronald L. Rivest, Robert E. Schapire
    Diversity-Based Inference of Finite Automata. [Citation Graph (0, 0)][DBLP]
    J. ACM, 1994, v:41, n:3, pp:555-589 [Journal]
  71. William W. Cohen, Robert E. Schapire, Yoram Singer
    Learning to Order Things. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1999, v:10, n:, pp:243-270 [Journal]
  72. Peter Stone, Robert E. Schapire, Michael L. Littman, János A. Csirik, David A. McAllester
    Decision-Theoretic Bidding Based on Learned Density Models in Simultaneous, Interacting Auctions. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 2003, v:19, n:, pp:209-242 [Journal]
  73. Yoav Freund, Robert E. Schapire
    A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1997, v:55, n:1, pp:119-139 [Journal]
  74. Michael J. Kearns, Robert E. Schapire
    Efficient Distribution-Free Learning of Probabilistic Concepts. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1994, v:48, n:3, pp:464-497 [Journal]
  75. Robert E. Schapire, Linda Sellie
    Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. [Citation Graph (0, 0)][DBLP]
    J. Comput. Syst. Sci., 1996, v:52, n:2, pp:201-213 [Journal]
  76. Erin L. Allwein, Robert E. Schapire, Yoram Singer
    Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2000, v:1, n:, pp:113-141 [Journal]
  77. Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer
    An Efficient Boosting Algorithm for Combining Preferences. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2003, v:4, n:, pp:933-969 [Journal]
  78. Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire
    The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:1557-1595 [Journal]
  79. Michael Collins, Robert E. Schapire, Yoram Singer
    Logistic Regression, AdaBoost and Bregman Distances. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2002, v:48, n:1-3, pp:253-285 [Journal]
  80. Yoav Freund, Robert E. Schapire
    Large Margin Classification Using the Perceptron Algorithm. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:37, n:3, pp:277-296 [Journal]
  81. David Haussler, Michael J. Kearns, Robert E. Schapire
    Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:14, n:1, pp:83-113 [Journal]
  82. David P. Helmbold, Robert E. Schapire
    Predicting Nearly As Well As the Best Pruning of a Decision Tree. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:27, n:1, pp:51-68 [Journal]
  83. David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth
    A Comparison of New and Old Algorithms for a Mixture Estimation Problem. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:27, n:1, pp:97-119 [Journal]
  84. Michael J. Kearns, Robert E. Schapire, Linda Sellie
    Toward Efficient Agnostic Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:17, n:2-3, pp:115-141 [Journal]
  85. Robert E. Schapire
    Drifting Games. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:43, n:3, pp:265-291 [Journal]
  86. Robert E. Schapire
    The Strength of Weak Learnability. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1990, v:5, n:, pp:197-227 [Journal]
  87. Robert E. Schapire
    Learning Probabilistic Read-once Formulas on Product Distributions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:14, n:1, pp:47-81 [Journal]
  88. Robert E. Schapire, Yoram Singer
    Improved Boosting Algorithms Using Confidence-rated Predictions. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:37, n:3, pp:297-336 [Journal]
  89. Robert E. Schapire, Manfred K. Warmuth
    On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1996, v:22, n:1-3, pp:95-121 [Journal]
  90. Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire
    The Nonstochastic Multiarmed Bandit Problem. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 2002, v:32, n:1, pp:48-77 [Journal]
  91. Sally A. Goldman, Michael J. Kearns, Robert E. Schapire
    Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1993, v:22, n:4, pp:705-726 [Journal]
  92. Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire
    Learning Binary Relations and Total Orders. [Citation Graph (0, 0)][DBLP]
    SIAM J. Comput., 1993, v:22, n:5, pp:1006-1034 [Journal]
  93. Gökhan Tür, Dilek Z. Hakkani-Tür, Robert E. Schapire
    Combining active and semi-supervised learning for spoken language understanding. [Citation Graph (0, 0)][DBLP]
    Speech Communication, 2005, v:45, n:2, pp:171-186 [Journal]
  94. Miroslav Dudík, David M. Blei, Robert E. Schapire
    Hierarchical maximum entropy density estimation. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:249-256 [Conf]

  95. Learning with Continuous Experts Using Drifting Games. [Citation Graph (, )][DBLP]


  96. Apprenticeship learning using linear programming. [Citation Graph (, )][DBLP]


  97. FilterBoost: Regression and Classification on Large Datasets. [Citation Graph (, )][DBLP]


  98. A Game-Theoretic Approach to Apprenticeship Learning. [Citation Graph (, )][DBLP]


  99. From Optimization to Regret Minimization and Back Again. [Citation Graph (, )][DBLP]


  100. A contextual-bandit approach to personalized news article recommendation. [Citation Graph (, )][DBLP]


  101. Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields. [Citation Graph (, )][DBLP]


  102. Efficient Multiclass Implementations of L1-Regularized Maximum Entropy [Citation Graph (, )][DBLP]


  103. An Optimal High Probability Algorithm for the Contextual Bandit Problem [Citation Graph (, )][DBLP]


  104. A Contextual-Bandit Approach to Personalized News Article Recommendation [Citation Graph (, )][DBLP]


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