 | 2009 |
|---|
| 103 |       | Zafer Barutçuoglu,
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) |
| 2008 |
|---|
| 102 | | Indraneel Mukherjee,
Robert E. Schapire:
Learning with Continuous Experts Using Drifting Games.
ALT 2008: 240-255 |
| 101 | | Umar Syed,
Michael H. Bowling,
Robert E. Schapire:
Apprenticeship learning using linear programming.
ICML 2008: 1032-1039 |
| 100 | | Ioannis C. Avramopoulos,
Jennifer Rexford,
Robert E. Schapire:
From Optimization to Regret Minimization and Back Again.
SysML 2008 |
| 99 | | 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 |
|---|
| 98 | | Miroslav Dudík,
David M. Blei,
Robert E. Schapire:
Hierarchical maximum entropy density estimation.
ICML 2007: 249-256 |
| 97 | | Umar Syed,
Robert E. Schapire:
A Game-Theoretic Approach to Apprenticeship Learning.
NIPS 2007 |
| 96 | | Joseph K. Bradley,
Robert E. Schapire:
FilterBoost: Regression and Classification on Large Datasets.
NIPS 2007 |
| 2006 |
|---|
| 95 | | Miroslav Dudík,
Robert E. Schapire:
Maximum Entropy Distribution Estimation with Generalized Regularization.
COLT 2006: 123-138 |
| 94 | | Lev Reyzin,
Robert E. Schapire:
How boosting the margin can also boost classifier complexity.
ICML 2006: 753-760 |
| 93 | | Amit Agarwal,
Elad Hazan,
Satyen Kale,
Robert E. Schapire:
Algorithms for portfolio management based on the Newton method.
ICML 2006: 9-16 |
| 92 | | Zafer Barutçuoglu,
Robert E. Schapire,
Olga G. Troyanskaya:
Hierarchical multi-label prediction of gene function.
Bioinformatics 22(7): 830-836 (2006) |
| 2005 |
|---|
| 91 | | Cynthia Rudin,
Corinna Cortes,
Mehryar Mohri,
Robert E. Schapire:
Margin-Based Ranking Meets Boosting in the Middle.
COLT 2005: 63-78 |
| 90 | | Aurelie C. Lozano,
Sanjeev R. Kulkarni,
Robert E. Schapire:
Convergence and Consistency of Regularized Boosting Algorithms with Stationary B-Mixing Observations.
NIPS 2005 |
| 89 | | Miroslav Dudík,
Robert E. Schapire,
Steven J. Phillips:
Correcting sample selection bias in maximum entropy density estimation.
NIPS 2005 |
| 88 | | Patrick Haffner,
Steven J. Phillips,
Robert E. Schapire:
Efficient Multiclass Implementations of L1-Regularized Maximum Entropy
CoRR abs/cs/0506101: (2005) |
| 87 | | Robert E. Schapire,
Marie Rochery,
Mazin G. Rahim,
Narendra Gupta:
Boosting with prior knowledge for call classification.
IEEE Transactions on Speech and Audio Processing 13(2): 174-181 (2005) |
| 86 | | 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 | | Miroslav Dudík,
Steven J. Phillips,
Robert E. Schapire:
Performance Guarantees for Regularized Maximum Entropy Density Estimation.
COLT 2004: 472-486 |
| 84 | | Cynthia Rudin,
Robert E. Schapire,
Ingrid Daubechies:
Boosting Based on a Smooth Margin.
COLT 2004: 502-517 |
| 83 | | Steven J. Phillips,
Miroslav Dudík,
Robert E. Schapire:
A maximum entropy approach to species distribution modeling.
ICML 2004 |
| 82 | | 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 | | Cynthia Rudin,
Ingrid Daubechies,
Robert E. Schapire:
On the Dynamics of Boosting.
NIPS 2003 |
| 80 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | Robert E. Schapire:
Theoretical Views of Boosting and Applications.
ATL 1999: 13-25 |
| 62 | | Robert E. Schapire:
Drifting Games.
COLT 1999: 114-124 |
| 61 | | 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 | | 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 | | Yoav Freund,
Robert E. Schapire:
Large Margin Classification Using the Perceptron Algorithm.
COLT 1998: 209-217 |
| 55 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | David P. Helmbold,
Robert E. Schapire:
Predicting Nearly as Well as the Best Pruning of a Decision Tree.
COLT 1995: 61-68 |
| 36 | | 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 | | 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 | | 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 | | 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 | | 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 | | 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 | | Michael J. Kearns,
Robert E. Schapire,
Linda Sellie:
Toward Efficient Agnostic Learning.
COLT 1992: 341-352 |
| 15 | | Harris Drucker,
Robert E. Schapire,
Patrice Simard:
Improving Performance in Neural Networks Using a Boosting Algorithm.
NIPS 1992: 42-49 |
| 1991 |
|---|
| 14 | | Robert E. Schapire:
Learning Probabilistic Read-Once Formulas on Product Distributions.
COLT 1991: 184-198 |
| 13 | | 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 | | 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 | | Robert E. Schapire:
Pattern Languages are not Learnable.
COLT 1990: 122-129 |
| 10 | | Sally A. Goldman,
Michael J. Kearns,
Robert E. Schapire:
On the Sample Complexity of Weak Learning.
COLT 1990: 217-231 |
| 9 | | Sally A. Goldman,
Michael J. Kearns,
Robert E. Schapire:
Exact Identification of Circuits Using Fixed Points of Amplification Functions (Abstract).
COLT 1990: 388 |
| 8 | | 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 |
