Cynthia Rudin

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Chaofan ChenAssistant Professor of Computer Science, University of MainePreverjeni e-poštni naslov na maine.edu
Margo SeltzerUniversity of British ColumbiaPreverjeni e-poštni naslov na cs.ubc.ca
Berk UstunUCSDPreverjeni e-poštni naslov na ucsd.edu
Lesia SemenovaPostdoctoral Researcher, Microsoft ResearchPreverjeni e-poštni naslov na microsoft.com
Alina Jade BarnettDuke UniversityPreverjeni e-poštni naslov na duke.edu
Chudi ZhongUniversity of North Carolina at Chapel HillPreverjeni e-poštni naslov na unc.edu
Tyler H. McCormickUniversity of WashingtonPreverjeni e-poštni naslov na uw.edu
David MadiganProfessor of Statistics, Northeastern UniversityPreverjeni e-poštni naslov na northeastern.edu
Alexander VolfovskyDuke UniversityPreverjeni e-poštni naslov na duke.edu
Tong WangYale UniversityPreverjeni e-poštni naslov na yale.edu
Zhi ChenDuke UniversityPreverjeni e-poštni naslov na duke.edu
Rebecca J. PassonneauDepartment of Computer Science and Engineering, Penn StatePreverjeni e-poštni naslov na cse.psu.edu
Robert SchapireMicrosoft ResearchPreverjeni e-poštni naslov na microsoft.com
M. Brandon Westover, MD, PhDHarvard Medical School / Massachusetts General HospitalPreverjeni e-poštni naslov na mgh.harvard.edu
Haiyang HuangDuke UniversityPreverjeni e-poštni naslov na cs.duke.edu
Seyda ErtekinAssociate Professor @ ODTU and Computer Scientist & Researcher @ MITPreverjeni e-poštni naslov na metu.edu.tr
Haimonti DuttaDepartment of Management Science and Systems, University at Buffalo, New YorkPreverjeni e-poštni naslov na buffalo.edu
Yaron ShaposhnikUniversity of RochesterPreverjeni e-poštni naslov na simon.rochester.edu
Sudeepa RoyDuke University, Computer SciencePreverjeni e-poštni naslov na cs.duke.edu
Oscar LiMachine Learning Department, Carnegie Mellon UniversityPreverjeni e-poštni naslov na cmu.edu

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Cynthia Rudin

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

Preverjeni e-poštni naslov na cs.duke.edu - Domača stran

machine learning interpretability data science


Naslov Razvrsti po navedbah Razvrsti po letniku Razvrsti po naslovu	Navedeno Navedeno	Leto
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	8034	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	2059*	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	1434	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	1105*	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	899	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	698	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	650	2020
The Big Data Newsvendor: Practical Insights from Machine Learning C Rudin, GY Vahn Operations Research, 2014	617*	2014
Supersparse linear integer models for optimized medical scoring systems B Ustun, C Rudin Machine Learning 102, 349-391, 2016	484*	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	445	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	439	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	437	2021
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	433	2019
Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020	413	2020
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	412	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	406*	2017
Falling rule lists F Wang, C Rudin Artificial intelligence and statistics, 1013-1022, 2015	366	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	351	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	329	2015
Please stop explaining black box models for high stakes decisions C Rudin Stat 1050 (26), 4.1, 2018	262	2018

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