متابعة
Cynthia Rudin
Cynthia Rudin
Professor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke University
بريد إلكتروني تم التحقق منه على cs.duke.edu - الصفحة الرئيسية
عنوان
عدد مرات الاقتباسات
عدد مرات الاقتباسات
السنة
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
81062019
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
2070*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
14452019
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
1106*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
9072022
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
6992017
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
6532020
The Big Data Newsvendor: Practical Insights from Machine Learning‏
C Rudin, GY Vahn‏
Operations Research, 2014‏
618*2014
Supersparse linear integer models for optimized medical scoring systems‏
B Ustun, C Rudin‏
Machine Learning 102, 349-391, 2016‏
485*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
4462018
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
4442017
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
4402021
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
4362019
Concept whitening for interpretable image recognition‏
Z Chen, Y Bei, C Rudin‏
Nature Machine Intelligence 2 (12), 772-782, 2020‏
4182020
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
4132014
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
3662015
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
3512011
Interpretable classification models for recidivism prediction‏
J Zeng, B Ustun, C Rudin‏
Journal of the Royal Statistical Society, Series A: Statistics in Society, 2015‏
3292015
Optimal sparse decision trees‏
X Hu, C Rudin, M Seltzer‏
Advances in neural information processing systems 32, 2019‏
2622019
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مقالات 1–20