Cynthia Rudin

Resolving Predictive Multiplicity for the Rashomon Set AAAI 2026 AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation AAAI 2026 Cosine Similarity is Almost All You Need (for Prototypical-Part Models) WACV 2026 This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN MICCAI 2025 "What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts JMLR 2025 Leveraging Predictive Equivalence in Decision Trees ICML 2025 Near-Optimal Decision Trees in a SPLIT Second ICML 2025 Models That Are Interpretable But Not Transparent AISTATS 2025 Dimension Reduction with Locally Adjusted Graphs AAAI 2025 How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data AAAI 2025 Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time CVPR 2025 Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference AAAI 2024 Position: Amazing Things Come From Having Many Good Models ICML 2024 Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data AISTATS 2024 Safe and Interpretable Estimation of Optimal Treatment Regimes AISTATS 2024 Sparse and Faithful Explanations Without Sparse Models AISTATS 2024 Optimal Sparse Survival Trees AISTATS 2024 Interpretable Generalized Additive Models for Datasets with Missing Values NIPS 2024 Navigating the Effect of Parametrization for Dimensionality Reduction NIPS 2024 Improving Decision Sparsity NIPS 2024 Interpretable Image Classification with Adaptive Prototype-based Vision Transformers NIPS 2024 FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models NIPS 2024 Using Noise to Infer Aspects of Simplicity Without Learning NIPS 2024 Optimal Sparse Regression Trees AAAI 2023 A Path to Simpler Models Starts With Noise NIPS 2023 The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance NIPS 2023 This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations NIPS 2023 OKRidge: Scalable Optimal k-Sparse Ridge Regression NIPS 2023 Exploring and Interacting with the Set of Good Sparse Generalized Additive Models NIPS 2023 The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation ACL 2023 Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation JMLR 2023 Variable importance matching for causal inference UAI 2023 Data poisoning attacks on off-policy policy evaluation methods UAI 2022 Fast Sparse Decision Tree Optimization via Reference Ensembles AAAI 2022 Fast Sparse Classification for Generalized Linear and Additive Models AISTATS 2022 MALTS: Matching After Learning to Stretch JMLR 2022 Rethinking Nonlinear Instrumental Variable Models through Prediction Validity JMLR 2022 Exploring the Whole Rashomon Set of Sparse Decision Trees NIPS 2022 FasterRisk: Fast and Accurate Interpretable Risk Scores NIPS 2022 FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference JMLR 2021 Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization JMLR 2021 Multitask Learning for Citation Purpose Classification NAACL 2021 Regulating Greed Over Time in Multi-Armed Bandits JMLR 2021 Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference AISTATS 2020 A Transformer Approach to Contextual Sarcasm Detection in Twitter ACL 2020 PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models CVPR 2020 Metaphor Detection Using Contextual Word Embeddings From Transformers ACL 2020 Generalized and Scalable Optimal Sparse Decision Trees ICML 2020 Bandits for BMO Functions ICML 2020 Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation UAI 2020 Learning Optimized Risk Scores JMLR 2019 All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously JMLR 2019 This Looks Like That: Deep Learning for Interpretable Image Recognition NIPS 2019 Optimal Sparse Decision Trees NIPS 2019 Interpretable Almost Matching Exactly With Instrumental Variables UAI 2019 Interpretable Almost-Exact Matching for Causal Inference AISTATS 2019 Reducing Exploration of Dying Arms in Mortal Bandits UAI 2019 An Optimization Approach to Learning Falling Rule Lists AISTATS 2018 Learning Certifiably Optimal Rule Lists for Categorical Data JMLR 2018 Direct Learning to Rank And Rerank AISTATS 2018 Learning Cost-Effective and Interpretable Treatment Regimes AISTATS 2017 A Bayesian Framework for Learning Rule Sets for Interpretable Classification JMLR 2017 Scalable Bayesian Rule Lists ICML 2017 The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes JMLR 2016 CRAFT: ClusteR-specific Assorted Feature selecTion AISTATS 2016 Falling Rule Lists AISTATS 2015 The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification NIPS 2014 Machine Learning with Operational Costs JMLR 2013 Learning Theory Analysis for Association Rules and Sequential Event Prediction JMLR 2013 The Rate of Convergence of AdaBoost JMLR 2013 An Integer Optimization Approach to Associative Classification NIPS 2012 Open Problem: Does AdaBoost Always Cycle? COLT 2012 On Equivalence Relationships Between Classification and Ranking Algorithms JMLR 2011 Sequential Event Prediction with Association Rules COLT 2011 The Rate of Convergence of Adaboost COLT 2011 The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List JMLR 2009 Margin-based Ranking and an Equivalence between AdaBoost and RankBoost JMLR 2009 Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking ACL 2008 The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins JMLR 2004