Cynthia Rudin

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Berk UstunUCSDE-mail confirmado em ucsd.edu
Margo SeltzerUniversity of British ColumbiaE-mail confirmado em cs.ubc.ca
Chaofan ChenAssistant Professor of Computer Science, University of MaineE-mail confirmado em maine.edu
Lesia SemenovaPostdoctoral Researcher, Microsoft ResearchE-mail confirmado em microsoft.com
Alina Jade BarnettDuke UniversityE-mail confirmado em duke.edu
Tyler H. McCormickUniversity of WashingtonE-mail confirmado em uw.edu
David MadiganProfessor of Statistics, Northeastern UniversityE-mail confirmado em northeastern.edu
Chudi ZhongUniversity of North Carolina at Chapel HillE-mail confirmado em unc.edu
Alexander VolfovskyDuke UniversityE-mail confirmado em duke.edu
Tong WangYale UniversityE-mail confirmado em yale.edu
Zhi ChenDuke UniversityE-mail confirmado em duke.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StateE-mail confirmado em cse.psu.edu
Robert SchapireMicrosoft ResearchE-mail confirmado em microsoft.com
M. Brandon Westover, MD, PhDHarvard Medical School / Massachusetts General HospitalE-mail confirmado em mgh.harvard.edu
Seyda ErtekinAssociate Professor @ ODTU and Computer Scientist & Researcher @ MITE-mail confirmado em metu.edu.tr
Haimonti DuttaDepartment of Management Science and Systems, University at Buffalo, New YorkE-mail confirmado em buffalo.edu
Haiyang HuangDuke UniversityE-mail confirmado em cs.duke.edu
Sudeepa RoyDuke University, Computer ScienceE-mail confirmado em cs.duke.edu
Yaron ShaposhnikUniversity of RochesterE-mail confirmado em simon.rochester.edu
Oscar LiMachine Learning Department, Carnegie Mellon UniversityE-mail confirmado em cmu.edu

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Cynthia Rudin

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

E-mail confirmado em cs.duke.edu - Página inicial

machine learning interpretability data science


Título Ordenar por citações Ordenar por ano Ordenar por título	Citado por Citado por	Ano
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	7606	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	1939*	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	1336	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	1084	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	810	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	663	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	627	2020
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	604*	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	479	2019
Supersparse linear integer models for optimized medical scoring systems B Ustun, C Rudin Machine Learning 102, 349-391, 2016	477*	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	437	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	406	2017
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	405	2014
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	391*	2017
Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020	386	2020
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	371	2021
Falling rule lists F Wang, C Rudin Artificial intelligence and statistics, 1013-1022, 2015	365	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	348	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	323	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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