Cynthia Rudin
Cynthia Rudin
Professor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke University
Verified email at - Homepage
Cited by
Cited by
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
C Rudin
Nature machine intelligence 1 (5), 206-215, 2019
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.
A Fisher, C Rudin, F Dominici
J. Mach. Learn. Res. 20 (177), 1-81, 2019
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model
B Letham, C Rudin, TH McCormick, D Madigan
This looks like that: deep learning for interpretable image recognition
C Chen, O Li, D Tao, A Barnett, C Rudin, JK Su
Advances in neural information processing systems 32, 2019
Deep learning for case-based reasoning through prototypes: A neural network that explains its predictions
O Li, H Liu, C Chen, C Rudin
AAAI, 2017
The Big Data Newsvendor: Practical Insights from Machine Learning
C Rudin, GY Vahn
Operations Research, 2014
Supersparse linear integer models for optimized medical scoring systems
B Ustun, C Rudin
Machine Learning 102, 349-391, 2016
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
S Menon, A Damian, S Hu, N Ravi, C Rudin
CVPR 2020, 2020
The bayesian case model: A generative approach for case-based reasoning and prototype classification
B Kim, C Rudin, JA Shah
Advances in neural information processing systems 27, 2014
Learning certifiably optimal rule lists
E Angelino, N Larus-Stone, D Alabi, M Seltzer, C Rudin
Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge …, 2017
Machine learning for the New York City power grid
C Rudin, D Waltz, RN Anderson, A Boulanger, A Salleb-Aouissi, M Chow, ...
IEEE transactions on pattern analysis and machine intelligence 34 (2), 328-345, 2011
A bayesian framework for learning rule sets for interpretable classification
T Wang, C Rudin, F Doshi-Velez, Y Liu, E Klampfl, P MacNeille
The Journal of Machine Learning Research 18 (1), 2357-2393, 2017
Interpretable machine learning: Fundamental principles and 10 grand challenges
C Rudin, C Chen, Z Chen, H Huang, L Semenova, C Zhong
Statistic Surveys 16, 1-85, 2022
Falling rule lists
F Wang, C Rudin
Artificial intelligence and statistics, 1013-1022, 2015
The World Health Organization adult attention-deficit/hyperactivity disorder self-report screening scale for DSM-5
B Ustun, LA Adler, C Rudin, SV Faraone, TJ Spencer, P Berglund, ...
Jama psychiatry 74 (5), 520-526, 2017
Interpretable classification models for recidivism prediction
J Zeng, B Ustun, C Rudin
Journal of the Royal Statistical Society, Series A: Statistics in Society, 2015
The P-Norm Push: A simple convex ranking algorithm that concentrates at the top of the list
C Rudin
The Journal of Machine Learning Research 10, 2233-2271, 2009
Why are we using black box models in AI when we don’t need to? A lesson from an explainable AI competition
C Rudin, J Radin
Harvard Data Science Review 1 (2), 10.1162, 2019
Margin-based ranking and an equivalence between AdaBoost and RankBoost
C Rudin, RE Schapire
The Journal of Machine Learning Research 10, 2193-2232, 2009
Scalable Bayesian rule lists
H Yang, C Rudin, M Seltzer
ICML 2017, 2017
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