| Year |
Citation |
Score |
| 2015 |
Wang Z, Schapire RE, Verma N. Error Adaptive Classifier Boosting (EACB): Leveraging Data-Driven Training Towards Hardware Resilience for Signal Inference Ieee Transactions On Circuits and Systems I: Regular Papers. 62: 1136-1145. DOI: 10.1109/Tcsi.2015.2395591 |
0.305 |
|
| 2015 |
Kapicioglu B, Rosenberg DS, Schapire RE, Jebara T. Collaborative place models Ijcai International Joint Conference On Artificial Intelligence. 2015: 3612-3618. |
0.657 |
|
| 2014 |
Kapicioglu B, Rosenberg DS, Schapire RE, Jebara T. Collaborative ranking for local preferences Journal of Machine Learning Research. 33: 466-474. |
0.649 |
|
| 2013 |
Mukherjee I, Rudin C, Schapire RE. The rate of convergence of AdaBoost Journal of Machine Learning Research. 14: 2315-2347. |
0.402 |
|
| 2011 |
Jafarpour S, Cevher V, Schapire RE. A game theoretic approach to expander-based compressive sensing Ieee International Symposium On Information Theory - Proceedings. 464-468. DOI: 10.1109/ISIT.2011.6034169 |
0.609 |
|
| 2011 |
Jafarpour S, Schapire RE, Cevher V. Compressive sensing meets game theory Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 3660-3663. DOI: 10.1109/ICASSP.2011.5947144 |
0.616 |
|
| 2010 |
Mukherjee I, Schapire RE. Learning with continuous experts using drifting games Theoretical Computer Science. 411: 2670-2683. DOI: 10.1016/J.Tcs.2010.04.004 |
0.573 |
|
| 2010 |
Mukherjee I, Schapire RE. A theory of multiclass boosting Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.384 |
|
| 2010 |
Kapicioglu B, Schapire RE, Wikelski M, Broderick T. Combining spatial and telemetric features for learning animal movement models Proceedings of the 26th Conference On Uncertainty in Artificial Intelligence, Uai 2010. 260-267. |
0.666 |
|
| 2009 |
Barutcuoglu Z, Airoldi EM, Dumeaux V, Schapire RE, Troyanskaya OG. Aneuploidy prediction and tumor classification with heterogeneous hidden conditional random fields. Bioinformatics (Oxford, England). 25: 1307-13. PMID 19052061 DOI: 10.1093/Bioinformatics/Btn585 |
0.703 |
|
| 2008 |
Bourke C, Deng K, Scott SD, Schapire RE, Vinodchandran NV. On reoptimizing multi-class classifiers Machine Learning. 71: 219-242. DOI: 10.1007/S10994-008-5056-8 |
0.335 |
|
| 2006 |
Barutcuoglu Z, Schapire RE, Troyanskaya OG. Hierarchical multi-label prediction of gene function. Bioinformatics (Oxford, England). 22: 830-6. PMID 16410319 DOI: 10.1093/Bioinformatics/Btk048 |
0.699 |
|
| 2006 |
Phillips SJ, Anderson RP, Schapire RE. Maximum entropy modeling of species geographic distributions Ecological Modelling. 190: 231-259. DOI: 10.1016/J.Ecolmodel.2005.03.026 |
0.345 |
|
| 2004 |
Freund Y, Mansour Y, Schapire RE. Generalization bounds for averaged classifiers Annals of Statistics. 32: 1698-1722. DOI: 10.1214/009053604000000058 |
0.32 |
|
| 2004 |
Freund Y, Iyer R, Schapire RE, Singer Y. An efficient boosting algorithm for combining preferences Journal of Machine Learning Research. 4: 933-969. DOI: 10.1162/1532443041827916 |
0.347 |
|
| 2003 |
Stone P, Schapire RE, Littman ML, Csirik JA, McAllester D. Decision-theoretic bidding based on learned density Models in simultaneous, interacting auctions Journal of Artificial Intelligence Research. 19: 209-242. DOI: 10.1613/Jair.1200 |
0.374 |
|
| 2002 |
Stone P, Schapire RE, Csirik JA, Littman ML, McAllester D. ATTac-2001: A learning, autonomous bidding agent Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2531: 143-160. DOI: 10.1007/3-540-36378-5_9 |
0.343 |
|
| 2001 |
Allwein EL, Schapire RE, Singer Y. Reducing multiclass to binary: A unifying approach for margin classifiers Journal of Machine Learning Research. 1: 113-141. DOI: 10.1162/15324430152733133 |
0.355 |
|
| 1999 |
Cohen WW, Schapire RE, Singer Y. Learning to order things Journal of Artificial Intelligence Research. 10: 243-270. DOI: 10.1613/Jair.587 |
0.367 |
|
| 1997 |
Cesa-Bianchi N, Freund Y, Haussler D, Helmbold DP, Schapire RE, Warmuth MK. How to use expert advice Journal of the Acm. 44: 427-485. DOI: 10.1145/258128.258179 |
0.323 |
|
| 1997 |
Freund Y, Kearns M, Ron D, Rubinfeld R, Schapire RE, Sellie L. Efficient Learning of Typical Finite Automata from Random Walks Information and Computation. 138: 23-48. DOI: 10.1006/Inco.1997.2648 |
0.41 |
|
| 1996 |
Schapire RE. On the worst-case analysis of temporal-difference learning algorithms Machine Learning. 22: 95-121. DOI: 10.1007/Bf00114725 |
0.375 |
|
| 1995 |
Goldman SA, Kearns MJ, Schapire RE. On the Sample Complexity of Weak Learning Information and Computation. 117: 276-287. DOI: 10.1006/Inco.1995.1045 |
0.343 |
|
| 1993 |
Goldman SA, Rivest RL, Schapire RE. Learning Binary Relations and Total Orders Siam Journal On Computing. 22: 1006-1034. DOI: 10.1137/0222062 |
0.372 |
|
| 1993 |
Goldman SA, Kearns MJ, Schapire RE. Exact Identification of Read-Once Formulas Using Fixed Points of Amplification Functions Siam Journal On Computing. 22: 705-726. DOI: 10.1137/0222047 |
0.302 |
|
| 1990 |
Schapire RE. The Strength of Weak Learnability Machine Learning. 5: 197-227. DOI: 10.1023/A:1022648800760 |
0.379 |
|
| 1990 |
Rivest RL, Schapire RE. A new approach to unsupervised learning in deterministic environments Machine Learning. 670-684. DOI: 10.1016/B978-0-08-051055-2.50032-8 |
0.329 |
|
| Low-probability matches (unlikely to be authored by this person) |
| 2003 |
Auer P, Cesa-Bianchi N, Freund Y, Schapire RE. The nonstochastic multiarmed bandit problem Siam Journal On Computing. 32: 48-77. DOI: 10.1137/S0097539701398375 |
0.297 |
|
| 2017 |
Phillips SJ, Anderson RP, Dudík M, Schapire RE, Blair ME. Opening the black box: an open-source release of Maxent Ecography. 40: 887-893. DOI: 10.1111/Ecog.03049 |
0.295 |
|
| 2005 |
Schapire RE, Rochery M, Rahim M, Gupta N. Boosting with prior knowledge for call classification Ieee Transactions On Speech and Audio Processing. 13: 174-181. DOI: 10.1109/Tsa.2004.840937 |
0.287 |
|
| 1993 |
Rivest RL, Schapire RE. Inference of Finite Automata Using Homing Sequences Information and Computation. 103: 299-347. DOI: 10.1006/Inco.1993.1021 |
0.286 |
|
| 2016 |
Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 904: 23-37. |
0.279 |
|
| 2000 |
Schapire RE, Singer Y. BoosTexter: a boosting-based system for text categorization Machine Learning. 39: 135-168. DOI: 10.1023/A:1007649029923 |
0.279 |
|
| 2007 |
Rudin C, Schapire RE, Daubechies I. Analysis of boosting algorithms using the smooth margin function Annals of Statistics. 35: 2723-2768. DOI: 10.1214/009053607000000785 |
0.275 |
|
| 1993 |
DRUCKER H, SCHAPIRE R, SIMARD P. BOOSTING PERFORMANCE IN NEURAL NETWORKS International Journal of Pattern Recognition and Artificial Intelligence. 7: 705-719. DOI: 10.1142/S0218001493000352 |
0.275 |
|
| 1999 |
Freund Y, Schapire RE. Adaptive Game Playing Using Multiplicative Weights Games and Economic Behavior. 29: 79-103. DOI: 10.1006/Game.1999.0738 |
0.274 |
|
| 1998 |
Schapire RE, Freund Y, Bartlett P, Lee WS. Boosting the margin: A new explanation for the effectiveness of voting methods Annals of Statistics. 26: 1651-1686. DOI: 10.1214/Aos/1024691352 |
0.273 |
|
| 2014 |
Lozano AC, Kulkarni SR, Schapire RE. Convergence and consistency of regularized boosting with weakly dependent observations Ieee Transactions On Information Theory. 60: 651-660. DOI: 10.1109/Tit.2013.2287726 |
0.268 |
|
| 2012 |
Agarwal A, Dudík M, Kale S, Langford J, Schapire RE. Contextual bandit learning with predictable rewards Journal of Machine Learning Research. 22: 19-26. |
0.265 |
|
| 1998 |
Helmbold DP, Schapire RE, Singer Y, Warmuth MK. On-line portfolio selection using multiplicative updates Mathematical Finance. 8: 325-347. DOI: 10.1111/1467-9965.00058 |
0.262 |
|
| 1994 |
Rivest RL, Schapire RE. Diversity-Based Inference of Finite Automata Journal of the Acm (Jacm). 41: 555-589. DOI: 10.1145/176584.176589 |
0.261 |
|
| 1994 |
Kearns MJ, Schapire RE. Efficient distribution-free learning of probabilistic concepts Journal of Computer and System Sciences. 48: 464-497. DOI: 10.1016/S0022-0000(05)80062-5 |
0.251 |
|
| 1994 |
Kearns MJ, Schapire RE, Sellie LM. Toward Efficient Agnostic Learning Machine Learning. 17: 115-141. DOI: 10.1023/A:1022615600103 |
0.25 |
|
| 2013 |
Schapire RE. Explaining adaboost Empirical Inference: Festschrift in Honor of Vladimir N. Vapnik. 37-52. DOI: 10.1007/978-3-642-41136-6_5 |
0.235 |
|
| 2009 |
Syed U, Schapire RE. A game-theoretic approach to apprenticeship learning Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.234 |
|
| 1994 |
Haussler D, Kearns M, Schapire RE. Bounds on the Sample Complexity of Bayesian Learning Using Information Theory and the VC Dimension Machine Learning. 14: 83-113. DOI: 10.1023/A:1022698821832 |
0.233 |
|
| 2015 |
Huang TK, Agarwal A, Hsu D, Langford J, Schapire RE. Efficient and parsimonious agnostic active learning Advances in Neural Information Processing Systems. 2015: 2755-2763. |
0.225 |
|
| 2004 |
Phillips SJ, Dudík M, Schapire RE. A maximum entropy approach to species distribution modeling Proceedings, Twenty-First International Conference On Machine Learning, Icml 2004. 655-662. |
0.223 |
|
| 2005 |
Tur G, Hakkani-Tür D, Schapire RE. Combining active and semi-supervised learning for spoken language understanding Speech Communication. 45: 171-186. DOI: 10.1016/j.specom.2004.08.002 |
0.223 |
|
| 2014 |
Luo H, Schapire RE. A drifting-games analysis for online learning and applications to boosting Advances in Neural Information Processing Systems. 2: 1368-1376. |
0.215 |
|
| 2010 |
Syed U, Schapire RE. A reduction from apprenticeship learning to classification Advances in Neural Information Processing Systems 23: 24th Annual Conference On Neural Information Processing Systems 2010, Nips 2010. |
0.211 |
|
| 2003 |
Tur G, Schapire RE, Hakkani-Tür D. Active learning for spoken language understanding Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 1: 276-279. |
0.204 |
|
| 2007 |
Ortiz LE, Schapire RE, Kakade SM. Maximum entropy correlated equilibria Journal of Machine Learning Research. 2: 347-354. |
0.202 |
|
| 2011 |
Beygelzimer A, Langford J, Li L, Reyzin L, Schapire RE. Contextual bandit algorithms with supervised learning guarantees Journal of Machine Learning Research. 15: 19-26. |
0.195 |
|
| 1999 |
Schapire RE. Theoretical views of boosting Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1572: 1-10. |
0.194 |
|
| 2010 |
Li L, Chu W, Langford J, Schapire RE. A contextual-bandit approach to personalized news article recommendation Proceedings of the 19th International Conference On World Wide Web, Www '10. 661-670. DOI: 10.1145/1772690.1772758 |
0.191 |
|
| 1996 |
Schapire RE, Sellie LM. Learning sparse multivariate polynomials over a field with queries and counterexamples Journal of Computer and System Sciences. 52: 201-213. DOI: 10.1006/jcss.1996.0017 |
0.189 |
|
| 1999 |
Schapire RE. Theoretical, views of boosting and applications Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 1720: 13-25. |
0.188 |
|
| 2007 |
Syed U, Schapire RE. Imitation learning with a value-based prior Proceedings of the 23rd Conference On Uncertainty in Artificial Intelligence, Uai 2007. 384-391. |
0.187 |
|
| 2004 |
Dudik M, Phillips SJ, Schapire RE. Performance guarantees for regularized maximum entropy density estimation Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 3120: 472-486. |
0.184 |
|
| 1999 |
Schapire RE. A brief introduction to boosting Ijcai International Joint Conference On Artificial Intelligence. 2: 1401-1406. |
0.183 |
|
| 1997 |
Freund Y, Kearns M, Ron D, Rubinfeld R, Schapire RE, Sellie L. Efficient Learning of Typical Finite Automata from Random Walks Information and Computation. 138: 23-48. DOI: 10.1006/inco.1997.2648 |
0.182 |
|
| 2006 |
Dudík M, Schapire RE. Maximum entropy distribution estimation with generalized regularization Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4005: 123-138. |
0.179 |
|
| 2014 |
Luo H, Schapire RE. Towards minimax online learning with unknown time horizon 31st International Conference On Machine Learning, Icml 2014. 1: 378-397. |
0.177 |
|
| 2004 |
Bartlett PL, Bickel PJ, Bühlmann P, Freund Y, Friedman J, Hastie T, Jiang W, Jordan MJ, Koltchinskii V, Lugosi G, McAuliffe JD, Ritov Y, Rosset S, Schapire RE, Tibshirani R, et al. Discussions of boosting papers, and rejoinders The Annals of Statistics. 32: 85-134. DOI: 10.1214/Aos/1105988581 |
0.176 |
|
| 2007 |
Dudík M, Phillips SJ, Schapire RE. Maximum entropy density estimation with generalized regularization and an application to species distribution modeling Journal of Machine Learning Research. 8: 1217-1260. |
0.173 |
|
| 2005 |
Rudin C, Cortes C, Mohri M, Schapire RE. Margin-based ranking meets boosting in the middle Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 3559: 63-78. |
0.173 |
|
| 2008 |
Wisz MS, Hijmans RJ, Li J, Peterson AT, Graham CH, Guisan A, Elith J, Dudík M, Ferrier S, Huettmann F, Leathwick JR, Lehmann A, Lohmann L, Loiselle BA, Manion G, ... ... Schapire RE, et al. Effects of sample size on the performance of species distribution models Diversity and Distributions. 14: 763-773. DOI: 10.1111/J.1472-4642.2008.00482.X |
0.168 |
|
| 2004 |
Rudin C, Daubechies I, Schapire RE. On the dynamics of boosting Advances in Neural Information Processing Systems. |
0.164 |
|
| 2009 |
Bradley JK, Schapire RE. FilterBoost: Regression and classification on large datasets Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference. |
0.164 |
|
| 2007 |
Guisan A, Graham CH, Elith J, Huettmann F, Dudik M, Ferrier S, Hijmans R, Lehmann A, Li J, Lohmann LG, Loiselle B, Manion G, Moritz C, Nakamura M, Nakazawa Y, ... ... Schapire RE, et al. Sensitivity of predictive species distribution models to change in grain size Diversity and Distributions. 13: 332-340. DOI: 10.1111/J.1472-4642.2007.00342.X |
0.161 |
|
| 2015 |
Syrgkanis V, Agarwal A, Luo H, Schapire RE. Fast convergence of regularized learning in games Advances in Neural Information Processing Systems. 2015: 2989-2997. |
0.16 |
|
| 2009 |
Rudin C, Schapire RE. Margin-based ranking and an equivalence between AdaBoost and RankBoost Journal of Machine Learning Research. 10: 2193-2232. |
0.156 |
|
| 2014 |
Agarwal A, Badanidiyuru A, Dudík M, Schapire RE, Slivkins A. Robust multi-objective learning with mentor feedback Journal of Machine Learning Research. 35: 726-741. |
0.156 |
|
| 2008 |
Syed U, Bowling M, Schapire RE. Apprenticeship learning using linear programming Proceedings of the 25th International Conference On Machine Learning. 1032-1039. |
0.154 |
|
| 2016 |
Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 904: 23-37. |
0.151 |
|
| 1994 |
Haussler D, Kearns M, Schapire RE. Bounds on the sample complexity of Bayesian learning using information theory and the VC dimension Machine Learning. 14: 83-113. DOI: 10.1007/BF00993163 |
0.148 |
|
| 2011 |
Chu W, Li L, Reyzin L, Schapire RE. Contextual bandits with linear Payoff functions Journal of Machine Learning Research. 15: 208-214. |
0.147 |
|
| 2006 |
Reyzin L, Schapire RE. How boosting the margin can also boost classifier complexity Acm International Conference Proceeding Series. 148: 753-760. DOI: 10.1145/1143844.1143939 |
0.147 |
|
| 2005 |
Lozano AC, Kulkarni SR, Schapire RE. Convergence and consistency of regularized Boosting algorithms with stationary β-mixing observations Advances in Neural Information Processing Systems. 819-826. |
0.138 |
|
| 1999 |
Schapire RE, Singer Y. Improved boosting algorithms using confidence-rated predictions Machine Learning. 37: 297-336. DOI: 10.1023/A:1007614523901 |
0.138 |
|
| 2004 |
Rudin C, Schapire RE, Daubechies I. Boosting based on a smooth margin Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 3120: 502-517. |
0.137 |
|
| 2009 |
Xi YT, Xiang ZJ, Ramadge PJ, Schapire RE. Speed and sparsity of regularized boosting Journal of Machine Learning Research. 5: 615-622. |
0.134 |
|
| 2006 |
Agarwal A, Hazan E, Kale S, Schapire RE. Algorithms for portfolio management based on the Newton method Acm International Conference Proceeding Series. 148: 9-16. DOI: 10.1145/1143844.1143846 |
0.132 |
|
| 1999 |
Freund Y, Schapire RE. Large margin classification using the perceptron algorithm Machine Learning. 37: 277-296. DOI: 10.1023/A:1007662407062 |
0.125 |
|
| 2014 |
Wang Z, Schapire R, Verma N. Error-adaptive classifier boosting (EACB): Exploiting data-driven training for highly fault-tolerant hardware Icassp, Ieee International Conference On Acoustics, Speech and Signal Processing - Proceedings. 3884-3888. DOI: 10.1109/ICASSP.2014.6854329 |
0.123 |
|
| 2002 |
Collins M, Dasgupta S, Schapire RE. A generalization of principal component analysis to the exponential family Advances in Neural Information Processing Systems. |
0.118 |
|
| 1997 |
Helmbold DP, Schapire RE. Predicting Nearly As Well As the Best Pruning of a Decision Tree Machine Learning. 27: 51-68. |
0.114 |
|
| 1994 |
Schapire RE. Learning Probabilistic Read-once Formulas on Product Distributions Machine Learning. 14: 47-81. DOI: 10.1023/A:1022646704993 |
0.113 |
|
| 2002 |
Collins M, Schapire RE, Singer Y. Logistic regression, AdaBoost and Bregman distances Machine Learning. 48: 253-285. DOI: 10.1023/A:1013912006537 |
0.109 |
|
| 2005 |
Dudík M, Schapire RE, Phillips SJ. Correcting sample selection bias in maximum entropy density estimation Advances in Neural Information Processing Systems. 323-330. |
0.109 |
|
| 1997 |
Freund Y, Schapire RE, Singer Y, Warmuth MK. Using and combining predictors that specialize Conference Proceedings of the Annual Acm Symposium On Theory of Computing. 334-343. |
0.107 |
|
| 1997 |
Helmbold DP, Schapire RE, Singer Y, Warmuth MK. A Comparison of New and Old Algorithms for a Mixture Estimation Problem Machine Learning. 27: 97-119. |
0.103 |
|
| 2014 |
Agarwal A, Hsu D, Kale S, Langford J, Li L, Schapire RE. Taming the monster: A fast and simple algorithm for contextual bandits 31st International Conference On Machine Learning, Icml 2014. 5: 3611-3619. |
0.098 |
|
| 2010 |
Schapire RE. The convergence rate of AdaBoost Colt 2010 - the 23rd Conference On Learning Theory. 308-309. |
0.081 |
|
| 2013 |
Mukherjee I, Rudin C, Schapire RE. The rate of convergence of AdaBoost Journal of Machine Learning Research. 14: 2315-2347. |
0.062 |
|
| 2001 |
Schapire RE. Drifting games Machine Learning. 43: 265-291. DOI: 10.1023/A:1010800213066 |
0.049 |
|
| 2007 |
Dudik M, Blei DM, Schapire RE. Hierarchical maximum entropy density estimation Acm International Conference Proceeding Series. 227: 249-256. DOI: 10.1145/1273496.1273528 |
0.039 |
|
| 1997 |
Cesa-Bianchi N, Freund Y, Haussler D, Helmbold DP, Schapire RE, Warmuth MK. How to use expert advice Journal of the Acm. 44: 427-485. |
0.034 |
|
| 2008 |
Freund Y, Schapire RE. 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. |
0.034 |
|
| 2004 |
Rudin C, Daubechies I, Schapire RE. The dynamics of AdaBoost: Cyclic behavior and convergence of margins Journal of Machine Learning Research. 5: 1557-1595. |
0.028 |
|
| 1998 |
Schapire RE, Singer Y, Singhal A. Boosting and Rocchio applied to text filtering Sigir Forum (Acm Special Interest Group On Information Retrieval). 215-223. |
0.02 |
|
| Hide low-probability matches. |