2008 | ||
---|---|---|
101 | EE | Indraneel Mukherjee, Robert E. Schapire: Learning with Continuous Experts Using Drifting Games. ALT 2008: 240-255 |
100 | EE | Umar Syed, Michael H. Bowling, Robert E. Schapire: Apprenticeship learning using linear programming. ICML 2008: 1032-1039 |
99 | EE | Ioannis C. Avramopoulos, Jennifer Rexford, Robert E. Schapire: From Optimization to Regret Minimization and Back Again. SysML 2008 |
98 | EE | Chris Bourke, Kun Deng, Stephen D. Scott, Robert E. Schapire, N. V. Vinodchandran: On reoptimizing multi-class classifiers. Machine Learning 71(2-3): 219-242 (2008) |
2007 | ||
97 | EE | Miroslav Dudík, David M. Blei, Robert E. Schapire: Hierarchical maximum entropy density estimation. ICML 2007: 249-256 |
96 | EE | Umar Syed, Robert E. Schapire: A Game-Theoretic Approach to Apprenticeship Learning. NIPS 2007 |
95 | EE | Joseph K. Bradley, Robert E. Schapire: FilterBoost: Regression and Classification on Large Datasets. NIPS 2007 |
2006 | ||
94 | EE | Miroslav Dudík, Robert E. Schapire: Maximum Entropy Distribution Estimation with Generalized Regularization. COLT 2006: 123-138 |
93 | EE | Lev Reyzin, Robert E. Schapire: How boosting the margin can also boost classifier complexity. ICML 2006: 753-760 |
92 | EE | Amit Agarwal, Elad Hazan, Satyen Kale, Robert E. Schapire: Algorithms for portfolio management based on the Newton method. ICML 2006: 9-16 |
91 | EE | Zafer Barutçuoglu, Robert E. Schapire, Olga G. Troyanskaya: Hierarchical multi-label prediction of gene function. Bioinformatics 22(7): 830-836 (2006) |
2005 | ||
90 | EE | Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire: Margin-Based Ranking Meets Boosting in the Middle. COLT 2005: 63-78 |
89 | EE | Aurelie C. Lozano, Sanjeev R. Kulkarni, Robert E. Schapire: Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations. NIPS 2005 |
88 | EE | Miroslav Dudík, Robert E. Schapire, Steven J. Phillips: Correcting sample selection bias in maximum entropy density estimation. NIPS 2005 |
87 | EE | Patrick Haffner, Steven J. Phillips, Robert E. Schapire: Efficient Multiclass Implementations of L1-Regularized Maximum Entropy CoRR abs/cs/0506101: (2005) |
86 | EE | Gökhan Tür, Dilek Z. Hakkani-Tür, Robert E. Schapire: Combining active and semi-supervised learning for spoken language understanding. Speech Communication 45(2): 171-186 (2005) |
2004 | ||
85 | EE | Miroslav Dudík, Steven J. Phillips, Robert E. Schapire: Performance Guarantees for Regularized Maximum Entropy Density Estimation. COLT 2004: 472-486 |
84 | EE | Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies: Boosting Based on a Smooth Margin. COLT 2004: 502-517 |
83 | EE | Steven J. Phillips, Miroslav Dudík, Robert E. Schapire: A maximum entropy approach to species distribution modeling. ICML 2004 |
82 | EE | Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire: The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. Journal of Machine Learning Research 5: 1557-1595 (2004) |
2003 | ||
81 | EE | Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire: On the Dynamics of Boosting. NIPS 2003 |
80 | EE | 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. J. Artif. Intell. Res. (JAIR) 19: 209-242 (2003) |
79 | EE | Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer: An Efficient Boosting Algorithm for Combining Preferences. Journal of Machine Learning Research 4: 933-969 (2003) |
2002 | ||
78 | EE | Peter Stone, Robert E. Schapire, János A. Csirik, Michael L. Littman, David A. McAllester: ATTac-2001: A Learning, Autonomous Bidding Agent. AMEC 2002: 143-160 |
77 | Robert E. Schapire, Marie Rochery, Mazin G. Rahim, Narendra Gupta: Incorporating Prior Knowledge into Boosting. ICML 2002: 538-545 | |
76 | Robert E. Schapire, Peter Stone, David A. McAllester, Michael L. Littman, János A. Csirik: Modeling Auction Price Uncertainty Using Boosting-based Conditional Density Estimation. ICML 2002: 546-553 | |
75 | Robert E. Schapire: Advances in Boosting. UAI 2002: 446-452 | |
74 | Michael Collins, Robert E. Schapire, Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. Machine Learning 48(1-3): 253-285 (2002) | |
73 | EE | Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire: The Nonstochastic Multiarmed Bandit Problem. SIAM J. Comput. 32(1): 48-77 (2002) |
2001 | ||
72 | EE | Michael Collins, S. Dasgupta, Robert E. Schapire: A Generalization of Principal Components Analysis to the Exponential Family. NIPS 2001: 617-624 |
71 | Robert E. Schapire: Drifting Games. Machine Learning 43(3): 265-291 (2001) | |
2000 | ||
70 | EE | Raj D. Iyer, David D. Lewis, Robert E. Schapire, Yoram Singer, Amit Singhal: Boosting for Document Routing. CIKM 2000: 70-77 |
69 | David A. McAllester, Robert E. Schapire: On the Convergence Rate of Good-Turing Estimators. COLT 2000: 1-6 | |
68 | Michael Collins, Robert E. Schapire, Yoram Singer: Logistic Regression, AdaBoost and Bregman Distances. COLT 2000: 158-169 | |
67 | Erin L. Allwein, Robert E. Schapire, Yoram Singer: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. ICML 2000: 9-16 | |
66 | EE | Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire: Gambling in a rigged casino: The adversarial multi-armed bandit problem Electronic Colloquium on Computational Complexity (ECCC) 7(68): (2000) |
65 | EE | Erin L. Allwein, Robert E. Schapire, Yoram Singer: Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers. Journal of Machine Learning Research 1: 113-141 (2000) |
64 | Robert E. Schapire, Yoram Singer: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39(2/3): 135-168 (2000) | |
1999 | ||
63 | EE | Robert E. Schapire: Theoretical Views of Boosting and Applications. ATL 1999: 13-25 |
62 | EE | Robert E. Schapire: Drifting Games. COLT 1999: 114-124 |
61 | EE | Robert E. Schapire: Theoretical Views of Boosting. EuroCOLT 1999: 1-10 |
60 | Robert E. Schapire: A Brief Introduction to Boosting. IJCAI 1999: 1401-1406 | |
59 | EE | William W. Cohen, Robert E. Schapire, Yoram Singer: Learning to Order Things. J. Artif. Intell. Res. (JAIR) 10: 243-270 (1999) |
58 | Yoav Freund, Robert E. Schapire: Large Margin Classification Using the Perceptron Algorithm. Machine Learning 37(3): 277-296 (1999) | |
57 | Robert E. Schapire, Yoram Singer: Improved Boosting Algorithms Using Confidence-rated Predictions. Machine Learning 37(3): 297-336 (1999) | |
1998 | ||
56 | EE | Yoav Freund, Robert E. Schapire: Large Margin Classification Using the Perceptron Algorithm. COLT 1998: 209-217 |
55 | EE | Robert E. Schapire, Yoram Singer: Improved Boosting Algorithms using Confidence-Rated Predictions. COLT 1998: 80-91 |
54 | Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yoram Singer: An Efficient Boosting Algorithm for Combining Preferences. ICML 1998: 170-178 | |
53 | EE | Robert E. Schapire, Yoram Singer, Amit Singhal: Boosting and Rocchio Applied to Text Filtering. SIGIR 1998: 215-223 |
1997 | ||
52 | Robert E. Schapire: Using output codes to boost multiclass learning problems. ICML 1997: 313-321 | |
51 | Robert E. Schapire, Yoav Freund, Peter Barlett, Wee Sun Lee: Boosting the margin: A new explanation for the effectiveness of voting methods. ICML 1997: 322-330 | |
50 | William W. Cohen, Robert E. Schapire, Yoram Singer: Learning to Order Things. NIPS 1997 | |
49 | EE | Yoav Freund, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: Using and Combining Predictors That Specialize. STOC 1997: 334-343 |
48 | Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie: Efficient Learning of Typical Finite Automata from Random Walks. Inf. Comput. 138(1): 23-48 (1997) | |
47 | EE | Nicolò Cesa-Bianchi, Yoav Freund, David Haussler, David P. Helmbold, Robert E. Schapire, Manfred K. Warmuth: How to use expert advice. J. ACM 44(3): 427-485 (1997) |
46 | Yoav Freund, Robert E. Schapire: A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. Comput. Syst. Sci. 55(1): 119-139 (1997) | |
45 | David P. Helmbold, Robert E. Schapire: Predicting Nearly As Well As the Best Pruning of a Decision Tree. Machine Learning 27(1): 51-68 (1997) | |
44 | David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: A Comparison of New and Old Algorithms for a Mixture Estimation Problem. Machine Learning 27(1): 97-119 (1997) | |
1996 | ||
43 | EE | Yoav Freund, Robert E. Schapire: Game Theory, On-Line Prediction and Boosting. COLT 1996: 325-332 |
42 | Yoav Freund, Robert E. Schapire: Experiments with a New Boosting Algorithm. ICML 1996: 148-156 | |
41 | David P. Helmbold, Robert E. Schapire, Yoram Singer, Manfred K. Warmuth: On-Line Portfolio Selection Using Multiplicative Updates. ICML 1996: 243-251 | |
40 | EE | David D. Lewis, Robert E. Schapire, James P. Callan, Ron Papka: Training Algorithms for Linear Text Classifiers. SIGIR 1996: 298-306 |
39 | Robert E. Schapire, Linda Sellie: Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. J. Comput. Syst. Sci. 52(2): 201-213 (1996) | |
38 | Robert E. Schapire, Manfred K. Warmuth: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. Machine Learning 22(1-3): 95-121 (1996) | |
1995 | ||
37 | EE | David P. Helmbold, Robert E. Schapire: Predicting Nearly as Well as the Best Pruning of a Decision Tree. COLT 1995: 61-68 |
36 | EE | David P. Helmbold, Yoram Singer, Robert E. Schapire, Manfred K. Warmuth: A Comparison of New and Old Algorithms for a Mixture Estimation Problem. COLT 1995: 69-78 |
35 | Yoav Freund, Robert E. Schapire: A decision-theoretic generalization of on-line learning and an application to boosting. EuroCOLT 1995: 23-37 | |
34 | Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, Robert E. Schapire: Gambling in a Rigged Casino: The Adversarial Multi-Arm Bandit Problem. FOCS 1995: 322-331 | |
33 | 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. FOCS 1995: 332-341 | |
32 | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weakly Learning Inf. Comput. 117(2): 276-287 (1995) | |
1994 | ||
31 | Robert E. Schapire, Manfred K. Warmuth: On the Worst-Case Analysis of Temporal-Difference Learning Algorithms. ICML 1994: 266-274 | |
30 | EE | Michael J. Kearns, Yishay Mansour, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie: On the learnability of discrete distributions. STOC 1994: 273-282 |
29 | EE | Ronald L. Rivest, Robert E. Schapire: Diversity-Based Inference of Finite Automata. J. ACM 41(3): 555-589 (1994) |
28 | Michael J. Kearns, Robert E. Schapire: Efficient Distribution-Free Learning of Probabilistic Concepts. J. Comput. Syst. Sci. 48(3): 464-497 (1994) | |
27 | Robert E. Schapire: Learning Probabilistic Read-once Formulas on Product Distributions. Machine Learning 14(1): 47-81 (1994) | |
26 | David Haussler, Michael J. Kearns, Robert E. Schapire: Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. Machine Learning 14(1): 83-113 (1994) | |
25 | Michael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. Machine Learning 17(2-3): 115-141 (1994) | |
1993 | ||
24 | EE | Robert E. Schapire, Linda Sellie: Learning Sparse Multivariate Polynomials over a Field with Queries and Counterexamples. COLT 1993: 17-26 |
23 | Ronald L. Rivest, Robert E. Schapire: Inference of Finite Automata Using Homing Sequences. Machine Learning: From Theory to Applications 1993: 51-73 | |
22 | EE | Yoav Freund, Michael J. Kearns, Dana Ron, Ronitt Rubinfeld, Robert E. Schapire, Linda Sellie: Efficient learning of typical finite automata from random walks. STOC 1993: 315-324 |
21 | EE | Nicolò Cesa-Bianchi, Yoav Freund, David P. Helmbold, David Haussler, Robert E. Schapire, Manfred K. Warmuth: How to use expert advice. STOC 1993: 382-391 |
20 | Harris Drucker, Robert E. Schapire, Patrice Simard: Boosting Performance in Neural Networks. IJPRAI 7(4): 705-719 (1993) | |
19 | Ronald L. Rivest, Robert E. Schapire: Inference of Finite Automata Using Homing Sequences Inf. Comput. 103(2): 299-347 (1993) | |
18 | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions. SIAM J. Comput. 22(4): 705-726 (1993) | |
17 | Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire: Learning Binary Relations and Total Orders. SIAM J. Comput. 22(5): 1006-1034 (1993) | |
1992 | ||
16 | EE | Michael J. Kearns, Robert E. Schapire, Linda Sellie: Toward Efficient Agnostic Learning. COLT 1992: 341-352 |
15 | EE | Harris Drucker, Robert E. Schapire, Patrice Simard: Improving Performance in Neural Networks Using a Boosting Algorithm. NIPS 1992: 42-49 |
1991 | ||
14 | EE | Robert E. Schapire: Learning Probabilistic Read-Once Formulas on Product Distributions. COLT 1991: 184-198 |
13 | EE | David Haussler, Michael J. Kearns, Robert E. Schapire: Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension. COLT 1991: 61-74 |
12 | EE | David Haussler, Michael J. Kearns, Manfred Opper, Robert E. Schapire: Estimating Average-Case Learning Curves Using Bayesian, Statistical Physics and VC Dimension Methods. NIPS 1991: 855-862 |
1990 | ||
11 | EE | Robert E. Schapire: Pattern Languages are not Learnable. COLT 1990: 122-129 |
10 | EE | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: On the Sample Complexity of Weak Learning. COLT 1990: 217-231 |
9 | EE | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract). COLT 1990: 388 |
8 | EE | Michael J. Kearns, Robert E. Schapire: Efficient Distribution-Free Learning of Probabilistic Concepts (Abstract). COLT 1990: 389 |
7 | Sally A. Goldman, Michael J. Kearns, Robert E. Schapire: Exact Identification of Circuits Using Fixed Points of Amplification Functions (Extended Abstract) FOCS 1990: 193-202 | |
6 | Michael J. Kearns, Robert E. Schapire: Efficient Distribution-free Learning of Probabilistic Concepts (Extended Abstract) FOCS 1990: 382-391 | |
5 | Robert E. Schapire: The Strength of Weak Learnability. Machine Learning 5: 197-227 (1990) | |
1989 | ||
4 | Robert E. Schapire: The Strength of Weak Learnability (Extended Abstract) FOCS 1989: 28-33 | |
3 | Sally A. Goldman, Ronald L. Rivest, Robert E. Schapire: Learning Binary Relations and Total Orders (Extended Abstract) FOCS 1989: 46-51 | |
2 | Ronald L. Rivest, Robert E. Schapire: Inference of Finite Automata Using Homing Sequences (Extended Abstract) STOC 1989: 411-420 | |
1987 | ||
1 | Ronald L. Rivest, Robert E. Schapire: Diversity-Based Inference of Finite Automata (Extended Abstract) FOCS 1987: 78-87 |