| 2026 | AAAI | Resolving Predictive Multiplicity for the Rashomon Set. | Parian Haghighat, Hadis Anahideh, Cynthia Rudin |
| 2026 | AAAI | AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation. | Stephen Ni-Hahn, Rico Zhu, Jerry Yin, Yue Jiang, Cynthia Rudin, Simon Mak |
| 2026 | WACV | Cosine Similarity is Almost All You Need (for Prototypical-Part Models). | Luke Moffett, Frank Willard, Maximillian Machado, Emmanuel Mokel, Jon Donnelly, Zhicheng Guo, Adam Costarino, Julia Yang, Giyoung Kim, Alina Jade Barnett, Cynthia Rudin |
| 2025 | AAAI | How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data. | Ishaan Maitra, Raymond Lin, Eric Chen, Jon Donnelly, Sanja Scepanovic, Cynthia Rudin |
| 2025 | AAAI | Dimension Reduction with Locally Adjusted Graphs. | Yingfan Wang, Yiyang Sun, Haiyang Huang, Cynthia Rudin |
| 2025 | AISTATS | Models That Are Interpretable But Not Transparent. | Chudi Zhong, Panyu Chen, Cynthia Rudin |
| 2025 | CVPR | Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time. | Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, Hayden McTavish, Chaofan Chen, Cynthia Rudin |
| 2025 | ICML | Near-Optimal Decision Trees in a SPLIT Second. | Varun Babbar, Hayden McTavish, Cynthia Rudin, Margo I. Seltzer |
| 2025 | ICML | Leveraging Predictive Equivalence in Decision Trees. | Hayden McTavish, Zachery Boner, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin |
| 2025 | MICCAI | This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN. | Dennis Tang, Jon Donnelly, Alina Jade Barnett, Lesia Semenova, Jin Jing, Peter Hadar, Ioannis Karakis, Olga Selioutski, Kehan Zhao, M. Brandon Westover, Cynthia Rudin |
| 2024 | AAAI | Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference. | Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky |
| 2024 | AISTATS | Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. | Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky |
| 2024 | AISTATS | Safe and Interpretable Estimation of Optimal Treatment Regimes. | Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky |
| 2024 | AISTATS | Sparse and Faithful Explanations Without Sparse Models. | Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin |
| 2024 | AISTATS | Optimal Sparse Survival Trees. | Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin |
| 2024 | CVPR | FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. | Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin |
| 2024 | ICML | Position: Amazing Things Come From Having Many Good Models. | Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner |
| 2024 | KDD | SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization. | Stephen Hahn, Jerry Yin, Rico Zhu, Weihan Xu, Yue Jiang, Simon Mak, Cynthia Rudin |
| 2023 | AAAI | Optimal Sparse Regression Trees. | Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin |
| 2023 | ACL | The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. | Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, Cynthia Rudin |
| 2023 | KDD | An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. | Stephen Hahn, Rico Zhu, Simon Mak, Cynthia Rudin, Yue Jiang |
| 2023 | UAI | Variable importance matching for causal inference. | Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page |
| 2022 | AAAI | Fast Sparse Decision Tree Optimization via Reference Ensembles. | Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer |
| 2022 | AISTATS | Fast Sparse Classification for Generalized Linear and Additive Models. | Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin |
| 2022 | CIKM | Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. | Ali Behrouz, Mathias Lcuyer, Cynthia Rudin, Mango I. Seltzer |
| 2022 | UAI | Data poisoning attacks on off-policy policy evaluation methods. | Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju |
| 2021 | AIES | Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. | Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan |
| 2020 | AISTATS | Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. | M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky |
| 2020 | CVPR | PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. | Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin |
| 2020 | ICML | Generalized and Scalable Optimal Sparse Decision Trees. | Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer |
| 2020 | ICML | Bandits for BMO Functions. | Tianyu Wang, Cynthia Rudin |
| 2020 | UAI | Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. | Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky |
| 2019 | AISTATS | Interpretable Almost-Exact Matching for Causal Inference. | Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky |
| 2019 | HCOMP | Interpretable Image Recognition with Hierarchical Prototypes. | Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin |
| 2019 | KDD | Do Simpler Models Exist and How Can We Find Them? | Cynthia Rudin |
| 2019 | UAI | Interpretable Almost Matching Exactly With Instrumental Variables. | M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky |
| 2019 | UAI | Reducing Exploration of Dying Arms in Mortal Bandits. | Stefano Trac, Weiyu Yan, Cynthia Rudin |
| 2018 | AAAI | Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions. | Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin |
| 2018 | AISTATS | An Optimization Approach to Learning Falling Rule Lists. | Chaofan Chen, Cynthia Rudin |
| 2018 | AISTATS | Direct Learning to Rank And Rerank. | Cynthia Rudin, Yining Wang |
| 2018 | CVPR | New Techniques for Preserving Global Structure and Denoising With Low Information Loss in Single-Image Super-Resolution. | Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin |
| 2017 | AISTATS | Learning Cost-Effective and Interpretable Treatment Regimes. | Himabindu Lakkaraju, Cynthia Rudin |
| 2017 | ICML | Scalable Bayesian Rule Lists. | Hongyu Yang, Cynthia Rudin, Margo I. Seltzer |
| 2017 | KDD | Learning Certifiably Optimal Rule Lists. | Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin |
| 2017 | KDD | Optimized Risk Scores. | Berk Ustun, Cynthia Rudin |
| 2016 | AISTATS | CRAFT: ClusteR-specific Assorted Feature selecTion. | Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola |
| 2016 | ICDM | Bayesian Rule Sets for Interpretable Classification. | Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille |
| 2016 | KDD | Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. | Benjamin Letham, Lydia M. Letham, Cynthia Rudin |
| 2015 | AISTATS | Falling Rule Lists. | Fulton Wang, Cynthia Rudin |
| 2014 | ANT | Modeling Weather Impact on a Secondary Electrical Grid. | Dingquan Wang, Rebecca J. Passonneau, Michael Collins, Cynthia Rudin |
| 2014 | ISAIM | Toward a Theory of Pattern Discovery. | Jonathan H. Huggins, Cynthia Rudin |
| 2014 | ISAIM | Generalization Bounds for Learning with Linear and Quadratic Side Knowledge. | Theja Tulabandhula, Cynthia Rudin |
| 2014 | ISAIM | Robust Optimization using Machine Learning for Uncertainty Sets. | Theja Tulabandhula, Cynthia Rudin |
| 2014 | KDD | Box drawings for learning with imbalanced data. | Siong Thye Goh, Cynthia Rudin |
| 2014 | KDD | Algorithms for interpretable machine learning. | Cynthia Rudin |
| 2014 | SDM | A Statistical Learning Theory Framework for Supervised Pattern Discovery. | Jonathan H. Huggins, Cynthia Rudin |
| 2013 | AAAI | Predicting Power Failures with Reactive Point Processes. | Seyda Ertekin, Cynthia Rudin, Tyler H. McCormick |
| 2013 | AAAI | Machine Learning for Meeting Analysis. | Been Kim, Cynthia Rudin |
| 2013 | AAAI | An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis. | Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan |
| 2013 | AAAI | Supersparse Linear Integer Models for Predictive Scoring Systems. | Berk Ustun, Stefano Trac, Cynthia Rudin |
| 2013 | AAAI | Detecting Patterns of Crime with Series Finder. | Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri |
| 2012 | ISAIM | The Influence of Operational Cost on Estimation. | Cynthia Rudin, Theja Tulabandhula |
| 2012 | SEKE | Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid. | Boyi Xie, Rebecca J. Passonneau, Haimonti Dutta, Jing-Yeu Miaw, Axinia Radeva, Ashish Tomar, Cynthia Rudin |
| 2009 | CICLING | Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods. | Rebecca J. Passonneau, Cynthia Rudin, Axinia Radeva, Zhi An Liu |
| 2009 | ICMLA | Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task. | Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, Delfina Isaac |
| 2008 | ACL | Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. | Ryan Roth, Owen Rambow, Nizar Habash, Mona T. Diab, Cynthia Rudin |
| 2006 | COLT | Ranking with a P-Norm Push. | Cynthia Rudin |
| 2005 | COLT | Margin-Based Ranking Meets Boosting in the Middle. | Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire |
| 2004 | COLT | Boosting Based on a Smooth Margin. | Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies |