Rudin, Cynthia

91 publications

JMLR 2025 "What Is Different Between These Datasets?" a Framework for Explaining Data Distribution Shifts Varun Babbar, Zhicheng Guo, Cynthia Rudin

NeurIPS 2025 Data Fusion for Partial Identification of Causal Effects Quinn Lanners, Cynthia Rudin, Alexander Volfovsky, Harsh Parikh

AAAI 2025 Dimension Reduction with Locally Adjusted Graphs Yingfan Wang, Yiyang Sun, Haiyang Huang, Cynthia Rudin

AAAI 2025 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

ICML 2025 Leveraging Predictive Equivalence in Decision Trees Hayden Mctavish, Zachery Boner, Jon Donnelly, Margo Seltzer, Cynthia Rudin

AISTATS 2025 Models That Are Interpretable but Not Transparent Chudi Zhong, Panyu Chen, Cynthia Rudin

ICML 2025 Near-Optimal Decision Trees in a SPLIT Second Varun Babbar, Hayden Mctavish, Cynthia Rudin, Margo Seltzer

CVPR 2025 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

AAAI 2024 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

CVPRW 2024 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

NeurIPS 2024 FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models Jiachang Liu, Rui Zhang, Cynthia Rudin

NeurIPS 2024 Improving Decision Sparsity Yiyang Sun, Tong Wang, Cynthia Rudin

AISTATS 2024 Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

NeurIPS 2024 Interpretable Generalized Additive Models for Datasets with Missing Values Hayden McTavish, Jon Donnelly, Margo Seltzer, Cynthia Rudin

NeurIPS 2024 Interpretable Image Classification with Adaptive Prototype-Based Vision Transformers Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen

NeurIPS 2024 Navigating the Effect of Parametrization for Dimensionality Reduction Haiyang Huang, Yingfan Wang, Cynthia Rudin

AISTATS 2024 Optimal Sparse Survival Trees Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin

ICML 2024 Position: Amazing Things Come from Having Many Good Models Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

AISTATS 2024 Safe and Interpretable Estimation of Optimal Treatment Regimes Harsh Parikh, Quinn M Lanners, Zade Akras, Sahar Zafar, M Brandon Westover, Cynthia Rudin, Alexander Volfovsky

AISTATS 2024 Sparse and Faithful Explanations Without Sparse Models Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin

NeurIPSW 2024 This Looks like Those: Illuminating Prototypical Concepts Using Multiple Visualizations Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin

NeurIPS 2024 Using Noise to Infer Aspects of Simplicity Without Learning Zachery Boner, Harry Chen, Lesia Semenova, Ronald Parr, Cynthia Rudin

NeurIPS 2023 A Path to Simpler Models Starts with Noise Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin

NeurIPS 2023 Exploring and Interacting with the Set of Good Sparse Generalized Additive Models Chudi Zhong, Zhi Chen, Jiachang Liu, Margo Seltzer, Cynthia Rudin

JMLR 2023 Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation Cynthia Rudin, Yaron Shaposhnik

CHIL 2023 Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruna

NeurIPS 2023 OKRidge: Scalable Optimal K-Sparse Ridge Regression Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin

AAAI 2023 Optimal Sparse Regression Trees Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin

NeurIPS 2023 The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-Based Variable Importance Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward Browne

NeurIPS 2023 This Looks like Those: Illuminating Prototypical Concepts Using Multiple Visualizations Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin

UAI 2023 Variable Importance Matching for Causal Inference Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page

AISTATS 2022 Fast Sparse Classification for Generalized Linear and Additive Models Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin

ICLRW 2022 Data Poisoning Attacks on Off-Policy Policy Evaluation Algorithms Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju

UAI 2022 Data Poisoning Attacks on Off-Policy Policy Evaluation Methods Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju

NeurIPS 2022 Exploring the Whole Rashomon Set of Sparse Decision Trees Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, Cynthia Rudin

AAAI 2022 Fast Sparse Decision Tree Optimization via Reference Ensembles Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer

NeurIPS 2022 FasterRisk: Fast and Accurate Interpretable Risk Scores Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer, Cynthia Rudin

JMLR 2022 MALTS: Matching After Learning to Stretch Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

NeurIPSW 2022 Moving Towards a More Equal World, One Ride at a Time: Studying Public Transportation Initiatives Using Interpretable Causal Inference Gaurav Rajesh Parikh, Albert Sun, Jenny Huang, Lesia Semenova, Cynthia Rudin

JMLR 2022 Rethinking Nonlinear Instrumental Variable Models Through Prediction Validity Chunxiao Li, Cynthia Rudin, Tyler H. McCormick

JMLR 2021 FLAME: A Fast Large-Scale Almost Matching Exactly Approach to Causal Inference Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

JAIR 2021 Playing Codenames with Language Graphs and Word Embeddings Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin

JMLR 2021 Regulating Greed over Time in Multi-Armed Bandits Stefano Tracà, Cynthia Rudin, Weiyu Yan

JMLR 2021 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

UAI 2020 Adaptive Hyper-Box Matching for Interpretable Individualized Treatment Effect Estimation Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

AISTATS 2020 Almost-Matching-Exactly for Treatment Effect Estimation Under Network Interference Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

ICML 2020 Bandits for BMO Functions Tianyu Wang, Cynthia Rudin

ICML 2020 Generalized and Scalable Optimal Sparse Decision Trees Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer

JMLR 2019 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

UAI 2019 Interpretable Almost Matching Exactly with Instrumental Variables M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

AISTATS 2019 Interpretable Almost-Exact Matching for Causal Inference Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

JMLR 2019 Learning Optimized Risk Scores Berk Ustun, Cynthia Rudin

NeurIPS 2019 Optimal Sparse Decision Trees Xiyang Hu, Cynthia Rudin, Margo Seltzer

UAI 2019 Reducing Exploration of Dying Arms in Mortal Bandits Stefano Tracà, Cynthia Rudin, Weiyu Yan

NeurIPS 2019 This Looks like That: Deep Learning for Interpretable Image Recognition Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan K Su

AISTATS 2018 An Optimization Approach to Learning Falling Rule Lists Chaofan Chen, Cynthia Rudin

AAAI 2018 Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin

AISTATS 2018 Direct Learning to Rank and Rerank Cynthia Rudin, Yining Wang

CVPRW 2018 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

JMLR 2017 A Bayesian Framework for Learning Rule Sets for Interpretable Classification Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille

AISTATS 2017 Learning Cost-Effective and Interpretable Treatment Regimes Himabindu Lakkaraju, Cynthia Rudin

ICML 2017 Scalable Bayesian Rule Lists Hongyu Yang, Cynthia Rudin, Margo Seltzer

AISTATS 2016 CRAFT: ClusteR-Specific Assorted Feature selecTion Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola

MLJ 2016 Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, David J. Libon, Rodney Swenson, Catherine C. Price, Melissa Lamar, Dana L. Penney

MLJ 2016 Supersparse Linear Integer Models for Optimized Medical Scoring Systems Berk Ustun, Cynthia Rudin

JMLR 2016 The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes Ramin Moghaddass, Cynthia Rudin, David Madigan

AISTATS 2015 Falling Rule Lists Fulton Wang, Cynthia Rudin

MLJ 2015 Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge Theja Tulabandhula, Cynthia Rudin

MLJ 2014 Machine Learning for Science and Society Cynthia Rudin, Kiri L. Wagstaff

MLJ 2014 On Combining Machine Learning with Decision Making Theja Tulabandhula, Cynthia Rudin

NeurIPS 2014 The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim, Cynthia Rudin, Julie A Shah

JMLR 2013 Learning Theory Analysis for Association Rules and Sequential Event Prediction Cynthia Rudin, Benjamin Letham, David Madigan

ECML-PKDD 2013 Learning to Detect Patterns of Crime Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri

JMLR 2013 Machine Learning with Operational Costs Theja Tulabandhula, Cynthia Rudin

MLJ 2013 Sequential Event Prediction Benjamin Letham, Cynthia Rudin, David Madigan

JMLR 2013 The Rate of Convergence of AdaBoost Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire

NeurIPS 2012 An Integer Optimization Approach to Associative Classification Dimitris Bertsimas, Allison Chang, Cynthia Rudin

MLJ 2012 How to Reverse-Engineer Quality Rankings Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas, Gloria Chou

COLT 2012 Open Problem: Does AdaBoost Always Cycle? Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies

JMLR 2011 On Equivalence Relationships Between Classification and Ranking Algorithms Şeyda Ertekin, Cynthia Rudin

COLT 2011 Sequential Event Prediction with Association Rules Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan

COLT 2011 The Rate of Convergence of Adaboost Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire

MLJ 2010 A Process for Predicting Manhole Events in Manhattan Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac

JMLR 2009 Margin-Based Ranking and an Equivalence Between AdaBoost and RankBoost Cynthia Rudin, Robert E. Schapire

ICCVW 2009 Online Coordinate Boosting Raphael Pelossof, Michael Jones, Ilia Vovsha, Cynthia Rudin

JMLR 2009 The P-Norm Push: A Simple Convex Ranking Algorithm That Concentrates at the Top of the List Cynthia Rudin

COLT 2006 Ranking with a P-Norm Push Cynthia Rudin

COLT 2005 Margin-Based Ranking Meets Boosting in the Middle Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire

COLT 2004 Boosting Based on a Smooth Margin Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies

JMLR 2004 The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire

NeurIPS 2003 On the Dynamics of Boosting Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire