| 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 |
|
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