Cynthia Rudin

25 catalog papers. Identity-linked publications and additional name matches are distinguished below.

ORCID: 0000-0003-4283-2780

Publications

• "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts — Journal of Machine Learning Research 2025 (name-matched, not ORCID-linked)
• Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation — Journal of Machine Learning Research 2023 (name-matched, not ORCID-linked)
• Rethinking Nonlinear Instrumental Variable Models through Prediction Validity — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
• MALTS: Matching After Learning to Stretch — Journal of Machine Learning Research 2022 (name-matched, not ORCID-linked)
• Regulating Greed Over Time in Multi-Armed Bandits — Journal of Machine Learning Research 2021 (name-matched, not ORCID-linked)
• FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference — Journal of Machine Learning Research 2021 (name-matched, not ORCID-linked)
• Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization — Journal of Machine Learning Research 2021 (name-matched, not ORCID-linked)
• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously — Journal of Machine Learning Research 2019 (name-matched, not ORCID-linked)
• The Big Data Newsvendor: Practical Insights from Machine Learning — Operations Research 2019
• Learning Optimized Risk Scores — Journal of Machine Learning Research 2019 (name-matched, not ORCID-linked)
• A Bayesian Framework for Learning Rule Sets for Interpretable Classification — Journal of Machine Learning Research 2017 (name-matched, not ORCID-linked)
• Learning Certifiably Optimal Rule Lists for Categorical Data — Journal of Machine Learning Research 2017 (name-matched, not ORCID-linked)
• The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes — Journal of Machine Learning Research 2016 (name-matched, not ORCID-linked)
• Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model — The Annals of Applied Statistics 2015
• The latent state hazard model, with application to wind turbine reliability — The Annals of Applied Statistics 2015 (name-matched, not ORCID-linked)
• Reactive point processes: A new approach to predicting power failures in underground electrical systems — The Annals of Applied Statistics 2015 (name-matched, not ORCID-linked)
• The Rate of Convergence of AdaBoost — Journal of Machine Learning Research 2013 (name-matched, not ORCID-linked)
• Machine Learning with Operational Costs — Journal of Machine Learning Research 2013 (name-matched, not ORCID-linked)
• Learning Theory Analysis for Association Rules and Sequential Event Prediction — Journal of Machine Learning Research 2013 (name-matched, not ORCID-linked)
• Bayesian hierarchical rule modeling for predicting medical conditions — The Annals of Applied Statistics 2012
• On Equivalence Relationships Between Classification and Ranking Algorithms — Journal of Machine Learning Research 2011 (name-matched, not ORCID-linked)
• Margin-based Ranking and an Equivalence between AdaBoost and RankBoost — Journal of Machine Learning Research 2009 (name-matched, not ORCID-linked)
• The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List — Journal of Machine Learning Research 2009 (name-matched, not ORCID-linked)
• Analysis of boosting algorithms using the smooth margin function — The Annals of Statistics 2007
• The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins — Journal of Machine Learning Research 2004 (name-matched, not ORCID-linked)