Cynthia Rudin

Research.com Recognitions

• 2019 - Fellow of the American Statistical Association (ASA)

Overview

Best Publications

• Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

  Cynthia Rudin

  7808
  Citations

• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

  Aaron Fisher;Cynthia Rudin;Francesca Dominici

  1373
  Citations

• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

  Benjamin Letham;Cynthia Rudin;Tyler H. McCormick;David Madigan

  877
  Citations

• Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges

  Cynthia Rudin;Chaofan Chen;Zhi Chen;Haiyang Huang

  760
  Citations

• The Big Data Newsvendor: Practical Insights from Machine Learning

  Gah-Yi Ban;Cynthia Rudin

  572
  Citations

• This Looks Like That: Deep Learning for Interpretable Image Recognition

  Chaofan Chen;Oscar Li;Chaofan Tao;Alina Jade Barnett

  510
  Citations

• Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions

  Oscar Li;Hao Liu;Chaofan Chen;Cynthia Rudin

  502
  Citations

• PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

  Sachit Menon;Alexandru Damian;Shijia Hu;Nikhil Ravi

  491
  Citations

• Supersparse linear integer models for optimized medical scoring systems

  Berk Ustun;Cynthia Rudin

  384
  Citations

• Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition

  Cynthia Rudin;Joanna Radin

  374
  Citations

• Interpretable classification models for recidivism prediction

  Jiaming Zeng;Berk Ustun;Cynthia Rudin

  344
  Citations

• Machine Learning for the New York City Power Grid

  C. Rudin;D. Waltz;R. N. Anderson;A. Boulanger

  329
  Citations

• The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

  Been Kim;Cynthia Rudin;Julie A Shah

  293
  Citations

• This Looks Like That: Deep Learning for Interpretable Image Recognition

  Chaofan Chen;Oscar Li;Daniel Tao;Alina Barnett

  279
  Citations

• Learning Certifiably Optimal Rule Lists for Categorical Data

  Elaine Angelino;Nicholas Larus-Stone;Daniel Alabi;Margo I. Seltzer

  264
  Citations

• Concept whitening for interpretable image recognition

  Zhi Chen;Yijie Bei;Cynthia Rudin

  260
  Citations

• Falling Rule Lists

  Fulton Wang;Cynthia Rudin

  235
  Citations

• Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization

  Yingfan Wang;Haiyang Huang;Cynthia Rudin;Yaron Shaposhnik

  234
  Citations

• A Bayesian framework for learning rule sets for interpretable classification

  Tong Wang;Cynthia Rudin;Finale Doshi-Velez;Yimin Liu

  231
  Citations

• The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

  Cynthia Rudin

  213
  Citations

• Scalable Bayesian rule lists

  Hongyu Yang;Cynthia Rudin;Margo Seltzer

  158
  Citations

• The Big Data Newsvendor: Practical Insights from Machine Learning Analysis

  Cynthia Rudin;Gah-Yi Vahn

  8
  Citations