Cynthia Rudin
Affiliations: | New York University, New York, NY, United States | ||
Professor | Duke University, Durham, NC |
Area:
Computation & TheoryWebsite:
http://www.cns.nyu.edu/~rudin/main.htmlGoogle:
"Cynthia Rudin"Mean distance: 14.8 (cluster 29) | S | N | B | C | P |
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Publications
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Ban G, Rudin C. (2019) The Big Data Newsvendor: Practical Insights from Machine Learning Operations Research. 67: 90-108 |
Rudin C. (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead Nature Machine Intelligence. 1: 206-215 |
Rudin C, Ustun B. (2018) Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice Interfaces. 48: 449-466 |
Rudin C, Ertekin Ş. (2018) Learning customized and optimized lists of rules with mathematical programming Mathematical Programming Computation. 10: 659-702 |
Zeng J, Ustun B, Rudin C. (2017) Interpretable classification models for recidivism prediction Journal of the Royal Statistical Society Series a-Statistics in Society. 180: 689-722 |
Letham B, Letham PA, Rudin C, et al. (2016) Prediction uncertainty and optimal experimental design for learning dynamical systems. Chaos (Woodbury, N.Y.). 26: 063110 |
Souillard-Mandar W, Davis R, Rudin C, et al. (2016) Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test. Machine Learning. 102: 393-441 |
Letham B, Rudin C, McCormick TH, et al. (2015) Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model Annals of Applied Statistics. 9: 1350-1371 |
Ertekin Ş, Rudin C, McCormick TH. (2015) Reactive point processes: A new approach to predicting power failures in underground electrical systems Annals of Applied Statistics. 9: 122-144 |
Ustun B, Rudin C. (2015) Supersparse linear integer models for optimized medical scoring systems Machine Learning |