Cynthia Rudin

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Chaofan ChenAssistant Professor of Computer Science, University of MaineEmail verificata su maine.edu
Berk UstunUCSDEmail verificata su ucsd.edu
Margo SeltzerUniversity of British ColumbiaEmail verificata su cs.ubc.ca
Lesia SemenovaPostdoctoral Researcher, Microsoft ResearchEmail verificata su microsoft.com
Alina Jade BarnettDuke UniversityEmail verificata su duke.edu
Tyler H. McCormickUniversity of WashingtonEmail verificata su uw.edu
Chudi ZhongUniversity of North Carolina at Chapel HillEmail verificata su unc.edu
David MadiganProfessor of Statistics, Northeastern UniversityEmail verificata su northeastern.edu
Alexander VolfovskyDuke UniversityEmail verificata su duke.edu
Tong WangYale UniversityEmail verificata su yale.edu
Zhi ChenDuke UniversityEmail verificata su duke.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StateEmail verificata su cse.psu.edu
Robert SchapireMicrosoft ResearchEmail verificata su microsoft.com
M. Brandon Westover, MD, PhDHarvard Medical School / Massachusetts General HospitalEmail verificata su mgh.harvard.edu
Haiyang HuangDuke UniversityEmail verificata su cs.duke.edu
Seyda ErtekinAssociate Professor @ ODTU and Computer Scientist & Researcher @ MITEmail verificata su metu.edu.tr
Haimonti DuttaDepartment of Management Science and Systems, University at Buffalo, New YorkEmail verificata su buffalo.edu
Yaron ShaposhnikUniversity of RochesterEmail verificata su simon.rochester.edu
Sudeepa RoyDuke University, Computer ScienceEmail verificata su cs.duke.edu
Oscar LiMachine Learning Department, Carnegie Mellon UniversityEmail verificata su cmu.edu

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Cynthia Rudin

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

Email verificata su cs.duke.edu - Home page

machine learning interpretability data science


Titolo Ordina per citazioni Ordina per anno Ordina per titolo	Citata da Citata da	Anno
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	8127	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	2038*	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	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	1110	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	880	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	684	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	652	2020
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	638*	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	488*	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	443	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	428	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	417	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	411	2014
Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020	409	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	405*	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	328	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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