Cynthia Rudin

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Berk UstunUCSDVerified email at ucsd.edu
Tyler H. McCormickUniversity of WashingtonVerified email at uw.edu
Chaofan ChenAssistant Professor in Computer Science, the University of MaineVerified email at maine.edu
David MadiganProfessor of Statistics, Northeastern UniversityVerified email at northeastern.edu
Margo SeltzerUniversity of British ColumbiaVerified email at cs.ubc.ca
Tong WangUniversity of iowaVerified email at uiowa.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StateVerified email at cse.psu.edu
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
Seyda ErtekinAssociate Professor @ ODTU and Computer Scientist & Researcher @ MITVerified email at metu.edu.tr
Alina Jade BarnettDuke UniversityVerified email at duke.edu
Alexander VolfovskyDuke UniversityVerified email at duke.edu
Chudi ZhongDuke UniversityVerified email at duke.edu
Zhi ChenDuke UniversityVerified email at duke.edu
Haimonti DuttaDepartment of Management Science and Systems, University at Buffalo, New YorkVerified email at buffalo.edu
Ingrid DaubechiesDuke UniversityVerified email at math.duke.edu
Lesia SemenovaPh.D. Candidate in Computer Science, Duke UniversityVerified email at cs.duke.edu
Theja TulabandhulaUniversity of Illinois ChicagoVerified email at alum.mit.edu
Roger N. AndersonColumbia UniversityVerified email at ldeo.columbia.edu
Leon WuColumbia UniversityVerified email at columbia.edu
Oscar LiMachine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu

Cynthia Rudin

Professor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke University

Verified email at cs.duke.edu - Homepage

machine learning interpretability data science


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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	4208	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	1159*	2019
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model B Letham, C Rudin, TH McCormick, D Madigan	868*	2015
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	752	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	440	2017
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	399*	2014
Supersparse linear integer models for optimized medical scoring systems B Ustun, C Rudin Machine Learning 102, 349-391, 2016	386*	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	377	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	331	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	317	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	312	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	303*	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	294	2022
Falling rule lists F Wang, C Rudin Artificial intelligence and statistics, 1013-1022, 2015	291	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	271	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	267	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	248*	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	242	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	224*	2009
Scalable Bayesian rule lists H Yang, C Rudin, M Seltzer ICML 2017, 2017	202	2017

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