Cynthia Rudin
Duke University(US)
Publications by Year
Research Areas
Explainable Artificial Intelligence (XAI), Machine Learning and Data Classification, Machine Learning and Algorithms, Bayesian Modeling and Causal Inference, Statistical Methods and Inference
Most-Cited Works
- → Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead(2019)8,207 cited
- → All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously(2018)1,156 cited
- → Interpretable machine learning: Fundamental principles and 10 grand challenges(2022)798 cited
- → Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model(2015)758 cited
- → This Looks Like That: Deep Learning for Interpretable Image Recognition(2018)565 cited
- → The Big Data Newsvendor: Practical Insights from Machine Learning(2018)501 cited
- → Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition(2019)409 cited
- → Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions(2018)400 cited
- → The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale forDSM-5(2017)400 cited
- → NTIRE 2018 Challenge on Single Image Super-Resolution: Methods and Results(2018)367 cited