| Year |
Citation |
Score |
| 2019 |
Ban G, Rudin C. The Big Data Newsvendor: Practical Insights from Machine Learning Operations Research. 67: 90-108. DOI: 10.2139/Ssrn.2559116 |
0.312 |
|
| 2019 |
Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Nature Machine Intelligence. 1: 206-215. DOI: 10.1038/S42256-019-0048-X |
0.346 |
|
| 2018 |
Rudin C, Ustun B. Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice Interfaces. 48: 449-466. DOI: 10.1287/Inte.2018.0957 |
0.336 |
|
| 2018 |
Rudin C, Ertekin Ş. Learning customized and optimized lists of rules with mathematical programming Mathematical Programming Computation. 10: 659-702. DOI: 10.1007/S12532-018-0143-8 |
0.334 |
|
| 2017 |
Zeng J, Ustun B, Rudin C. Interpretable classification models for recidivism prediction Journal of the Royal Statistical Society Series a-Statistics in Society. 180: 689-722. DOI: 10.1111/Rssa.12227 |
0.365 |
|
| 2016 |
Letham B, Letham PA, Rudin C, Browne EP. Prediction uncertainty and optimal experimental design for learning dynamical systems. Chaos (Woodbury, N.Y.). 26: 063110. PMID 27368775 DOI: 10.1063/1.4953795 |
0.333 |
|
| 2016 |
Souillard-Mandar W, Davis R, Rudin C, Au R, Libon DJ, Swenson R, Price CC, Lamar M, Penney DL. Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test. Machine Learning. 102: 393-441. PMID 27057085 DOI: 10.1007/S10994-015-5529-5 |
0.338 |
|
| 2015 |
Letham B, Rudin C, McCormick TH, Madigan D. Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model Annals of Applied Statistics. 9: 1350-1371. DOI: 10.1214/15-Aoas848 |
0.344 |
|
| 2015 |
Ertekin Ş, Rudin C, McCormick TH. Reactive point processes: A new approach to predicting power failures in underground electrical systems Annals of Applied Statistics. 9: 122-144. DOI: 10.1214/14-AOAS789 |
0.305 |
|
| 2015 |
Ustun B, Rudin C. Supersparse linear integer models for optimized medical scoring systems Machine Learning. DOI: 10.1007/s10994-015-5528-6 |
0.311 |
|
| 2014 |
Tulabandhula T, Rudin C. On combining machine learning with decision making Machine Learning. 97: 33-64. DOI: 10.1007/s10994-014-5459-7 |
0.343 |
|
| 2014 |
Rudin C, Wagstaff KL. Machine learning for science and society Machine Learning. 95: 1-9. DOI: 10.1007/S10994-013-5425-9 |
0.34 |
|
| 2014 |
Kim B, Rudin C. Learning about meetings Data Mining and Knowledge Discovery. 28: 1134-1157. DOI: 10.1007/s10618-014-0348-z |
0.302 |
|
| 2013 |
Rudin C, Letham B, Madigan D. Learning theory analysis for association rules and sequential event prediction Journal of Machine Learning Research. 14: 3441-3492. DOI: 10.7916/D82N50C1 |
0.353 |
|
| 2013 |
Letham B, Rudin C, Madigan D. Sequential event prediction Machine Learning. 93: 357-380. DOI: 10.1007/S10994-013-5356-5 |
0.305 |
|
| 2013 |
Letham B, Rudin C, Heller KA. Growing a list Data Mining and Knowledge Discovery. 27: 372-395. DOI: 10.1007/s10618-013-0329-7 |
0.31 |
|
| 2012 |
Rudin C, Waltz D, Anderson RN, Boulanger A, Salleb-Aouissi A, Chow M, Dutta H, Gross PN, Huang B, Ierome S, Isaac DF, Kressner A, Passonneau RJ, Radeva A, Wu L. Machine learning for the New York City power grid. Ieee Transactions On Pattern Analysis and Machine Intelligence. 34: 328-45. PMID 21576741 DOI: 10.1109/Tpami.2011.108 |
0.387 |
|
| 2010 |
Rudin C, Passonneau RJ, Radeva A, Dutta H, Ierome S, Isaac D. A process for predicting manhole events in Manhattan Machine Learning. 80: 1-31. DOI: 10.1007/S10994-009-5166-Y |
0.332 |
|
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