Cynthia Rudin

Cited by

	All	Since 2019
Citations	25633	22482
h-index	57	49
i10-index	135	113

6000

3000

1500

4500

200920102011201220132014201520162017201820192020202120222023202469 68 132 149 203 222 352 335 494 785 1280 2169 3431 4803 5853 4860

Public access

View all

60 articles

0 articles

available

not available

Based on funding mandates

Co-authors

Berk UstunUCSDVerified email at ucsd.edu
Chaofan ChenAssistant Professor of Computer Science, University of MaineVerified email at maine.edu
Margo SeltzerUniversity of British ColumbiaVerified email at cs.ubc.ca
Alina Jade BarnettDuke UniversityVerified email at duke.edu
Tyler H. McCormickUniversity of WashingtonVerified email at uw.edu
Lesia SemenovaPostdoctoral Researcher, Microsoft ResearchVerified email at microsoft.com
David MadiganProfessor of Statistics, Northeastern UniversityVerified email at northeastern.edu
Chudi ZhongUniversity of North Carolina at Chapel HillVerified email at unc.edu
Alexander VolfovskyDuke UniversityVerified email at duke.edu
Tong WangYale UniversityVerified email at yale.edu
Zhi ChenDuke UniversityVerified email at duke.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StateVerified email at cse.psu.edu
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
M. Brandon Westover, MD, PhDHarvard Medical School / Massachusetts General HospitalVerified email at mgh.harvard.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
Sudeepa RoyDuke University, Computer ScienceVerified email at cs.duke.edu
Yaron ShaposhnikUniversity of RochesterVerified email at simon.rochester.edu
Oscar LiMachine Learning Department, Carnegie Mellon UniversityVerified email at cmu.edu
Aaron J FisherFoundation MedicineVerified email at alumni.jh.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	7167	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 Journal of Machine Learning Research 20 (177), 1-81, 2019	1855*	2019
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	1270	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	1065	2015
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	737	2022
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	634	2017
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	596	2020
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	578*	2014
Supersparse linear integer models for optimized medical scoring systems B Ustun, C Rudin Machine Learning 102, 349-391, 2016	473*	2016
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	456	2019
Learning certifiably optimal rule lists for categorical data E Angelino, N Larus-Stone, D Alabi, M Seltzer, C Rudin Journal of Machine Learning Research 18 (234), 1-78, 2018	424	2018
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	400	2014
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	394	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	378*	2017
Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020	367	2020
Falling rule lists F Wang, C Rudin Artificial intelligence and statistics, 1013-1022, 2015	353	2015
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	344	2011
Understanding how dimension reduction tools work: an empirical approach to deciphering t-SNE, UMAP, TriMAP, and PaCMAP for data visualization Y Wang, H Huang, C Rudin, Y Shaposhnik Journal of Machine Learning Research 22, 2021	342	2021
Interpretable classification models for recidivism prediction J Zeng, B Ustun, C Rudin Journal of the Royal Statistical Society, Series A: Statistics in Society, 2015	317	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	263*	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