Cynthia Rudin

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Chaofan ChenAssistant Professor of Computer Science, University of MaineDirección de correo verificada de maine.edu
Margo SeltzerUniversity of British ColumbiaDirección de correo verificada de cs.ubc.ca
Berk UstunUCSDDirección de correo verificada de ucsd.edu
Lesia SemenovaPostdoctoral Researcher, Microsoft ResearchDirección de correo verificada de microsoft.com
Alina Jade BarnettDuke UniversityDirección de correo verificada de duke.edu
Tyler H. McCormickUniversity of WashingtonDirección de correo verificada de uw.edu
Chudi ZhongUniversity of North Carolina at Chapel HillDirección de correo verificada de unc.edu
David MadiganProfessor of Statistics, Northeastern UniversityDirección de correo verificada de northeastern.edu
Alexander VolfovskyDuke UniversityDirección de correo verificada de duke.edu
Tong WangYale UniversityDirección de correo verificada de yale.edu
Zhi ChenDuke UniversityDirección de correo verificada de duke.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StateDirección de correo verificada de cse.psu.edu
Robert SchapireMicrosoft ResearchDirección de correo verificada de microsoft.com
M. Brandon Westover, MD, PhDHarvard Medical School / Massachusetts General HospitalDirección de correo verificada de mgh.harvard.edu
Haiyang HuangDuke UniversityDirección de correo verificada de cs.duke.edu
Seyda ErtekinAssociate Professor @ ODTU and Computer Scientist & Researcher @ MITDirección de correo verificada de metu.edu.tr
Haimonti DuttaDepartment of Management Science and Systems, University at Buffalo, New YorkDirección de correo verificada de buffalo.edu
Yaron ShaposhnikUniversity of RochesterDirección de correo verificada de simon.rochester.edu
Sudeepa RoyDuke University, Computer ScienceDirección de correo verificada de cs.duke.edu
Oscar LiMachine Learning Department, Carnegie Mellon UniversityDirección de correo verificada de cmu.edu

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Cynthia Rudin

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

Dirección de correo verificada de cs.duke.edu - Página principal

machine learning interpretability data science


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
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	8175	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	2051*	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	1413	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	1114	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	887	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	688	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	648	2020
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	637*	2014
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	514	2019
Supersparse linear integer models for optimized medical scoring systems B Ustun, C Rudin Machine Learning 102, 349-391, 2016	489*	2016
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	444	2018
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	429	2017
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	418	2021
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	413	2014
Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020	411	2020
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	407*	2017
Falling rule lists F Wang, C Rudin Artificial intelligence and statistics, 1013-1022, 2015	368	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	354	2011
Interpretable classification models for recidivism prediction J Zeng, B Ustun, C Rudin Journal of the Royal Statistical Society, Series A: Statistics in Society, 2015	331	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

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