Cynthia Rudin

Cited by

	All	Since 2018
Citations	20101	17736
h-index	55	48
i10-index	126	106

6000

3000

1500

4500

2007200820092010201120122013201420152016201720182019202020212022202356 46 70 70 132 150 200 220 356 341 517 776 1281 2180 3395 4817 5177

Public access

View all

49 articles

2 articles

available

not available

Based on funding mandates

Co-authors

Berk UstunUCSDVerified email at ucsd.edu
Chaofan ChenAssistant Professor in Computer Science, the University of MaineVerified email at maine.edu
Margo SeltzerUniversity of British ColumbiaVerified email at cs.ubc.ca
Tyler H. McCormickUniversity of WashingtonVerified email at uw.edu
David MadiganProfessor of Statistics, Northeastern UniversityVerified email at northeastern.edu
Tong WangYale UniversityVerified email at yale.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StateVerified email at cse.psu.edu
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
Chudi ZhongDuke UniversityVerified email at duke.edu
Alexander VolfovskyDuke UniversityVerified email at duke.edu
Alina Jade BarnettDuke UniversityVerified email at duke.edu
Lesia SemenovaPh.D. Candidate in Computer Science, Duke UniversityVerified email at cs.duke.edu
Zhi ChenDuke UniversityVerified email at duke.edu
Seyda ErtekinAssociate Professor @ ODTU and Computer Scientist & Researcher @ MITVerified email at metu.edu.tr
Haimonti DuttaDepartment of Management Science and Systems, University at Buffalo, New YorkVerified email at buffalo.edu
M. Brandon Westover, MD, PhDHarvard Medical School / Massachusetts General HospitalVerified email at mgh.harvard.edu
Ingrid DaubechiesDuke UniversityVerified email at math.duke.edu
Chaofan TaoDuke UniversityVerified email at duke.edu
Theja TulabandhulaUniversity of Illinois ChicagoVerified email at alum.mit.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


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	5192	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	1410*	2019
Interpretable classifiers using rules and bayesian analysis: Building a better stroke prediction model B Letham, C Rudin, TH McCormick, D Madigan Annals of Applied Statistics 9 (3), 1350-1371, 2015	955*	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	936	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	507	2017
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	488*	2014
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	468	2020
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	447	2022
Supersparse linear integer models for optimized medical scoring systems B Ustun, C Rudin Machine Learning 102, 349-391, 2016	410*	2016
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	361	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	359	2017
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	339*	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	327	2011
Falling rule lists F Wang, C Rudin Artificial intelligence and statistics, 1013-1022, 2015	314	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	309	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	282	2015
Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020	270	2020
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	254*	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), 1-9, 2019	250	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	231*	2009

The system can't perform the operation now. Try again later.

Articles 1–20

Citations per year

Duplicate citations

Merged citations

Add co-authorsCo-authors

Follow

Cited by

Co-authors