On the Dynamics of Boosting. - Cynthia Rudin; Ingrid Daubechies; Robert E. Schapire (NIPS, 2003)

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

The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. - Cynthia Rudin; Ingrid Daubechies; Robert E. Schapire (J. Mach. Learn. Res., 2004)

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

Stability Analysis for Regularized Least Squares Regression - Cynthia Rudin (CoRR, 2005)

Ranking with a P-Norm Push. - Cynthia Rudin (COLT, 2006)

Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. - Ryan Roth; Owen Rambow; Nizar Habash; Mona T. Diab; Cynthia Rudin (ACL (2), 2008)

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 (CICLing, 2009)

Online coordinate boosting. - Raphael Pelossof; Michael Jones; Ilia Vovsha; Cynthia Rudin (ICCV Workshops, 2009)

Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task. - Axinia Radeva; Cynthia Rudin; Rebecca J. Passonneau; Delfina Isaac (ICMLA, 2009)

The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List. - Cynthia Rudin (J. Mach. Learn. Res., 2009)

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost. - Cynthia Rudin; Robert E. Schapire (J. Mach. Learn. Res., 2009)

A process for predicting manhole events in Manhattan. - Cynthia Rudin; Rebecca J. Passonneau; Axinia Radeva; Haimonti Dutta; Steve Ierome; Delfina Isaac (Mach. Learn., 2010)

The Machine Learning and Traveling Repairman Problem. - Theja Tulabandhula; Cynthia Rudin; Patrick Jaillet (ADT, 2011)

21st-Century Data Miners Meet 19th-Century Electrical Cables. - Cynthia Rudin; Rebecca J. Passonneau; Axinia Radeva; Steve Ierome; Delfina Isaac (Computer, 2011)

Machine Learning and the Traveling Repairman - Theja Tulabandhula; Cynthia Rudin; Patrick Jaillet (CoRR, 2011)

On Equivalence Relationships Between Classification and Ranking Algorithms. - Seyda Ertekin; Cynthia Rudin (J. Mach. Learn. Res., 2011)

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

Selective Sampling of Labelers for Approximating the Crowd. - Seyda Ertekin; Haym Hirsh; Cynthia Rudin (AAAI Fall Symposium: Machine Aggregation of Human Judgment, 2012)

The Influence of Operational Cost on Estimation. - Cynthia Rudin; Theja Tulabandhula (ISAIM, 2012)

An Integer Optimization Approach to Associative Classification. - Allison Chang; Dimitris Bertsimas; Cynthia Rudin (NIPS, 2012)

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 (SEKE, 2012)

Learning to Predict the Wisdom of Crowds - Seyda Ertekin; Haym Hirsh; Cynthia Rudin (CoRR, 2012)

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

How to reverse-engineer quality rankings. - Allison Chang; Cynthia Rudin; Michael Cavaretta; Robert Thomas; Gloria Chou (Mach. Learn., 2012)

Machine Learning for theNew York City Power Grid. - Cynthia Rudin; David L. Waltz; Roger Anderson; Albert Boulanger; Ansaf Salleb-Aouissi; Maggie Chow; Haimonti Dutta; Philip Gross; Bert Huang; Steve Ierome (IEEE Trans. Pattern Anal. Mach. Intell., 2012)

Predicting Power Failures with Reactive Point Processes. - Seyda Ertekin; Cynthia Rudin; Tyler H. McCormick (AAAI (Late-Breaking Developments), 2013)

Machine Learning for Meeting Analysis. - Been Kim; Cynthia Rudin (AAAI (Late-Breaking Developments), 2013)

An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis. - Benjamin Letham; Cynthia Rudin; Tyler H. McCormick; David Madigan (AAAI (Late-Breaking Developments), 2013)

Supersparse Linear Integer Models for Predictive Scoring Systems. - Berk Ustun; Stefano Tracà; Cynthia Rudin (AAAI (Late-Breaking Developments), 2013)

Detecting Patterns of Crime with Series Finder. - Tong Wang 011; Cynthia Rudin; Daniel Wagner; Rich Sevieri (AAAI (Late-Breaking Developments), 2013)

Learning to Detect Patterns of Crime. - Tong Wang 011; Cynthia Rudin; Daniel Wagner; Rich Sevieri (ECML/PKDD (3), 2013)

Growing a list. - Benjamin Letham; Cynthia Rudin; Katherine A. Heller (Data Min. Knowl. Discov., 2013)

The rate of convergence of AdaBoost. - Indraneel Mukherjee; Cynthia Rudin; Robert E. Schapire (J. Mach. Learn. Res., 2013)

Learning theory analysis for association rules and sequential event prediction. - Cynthia Rudin; Benjamin Letham; David Madigan (J. Mach. Learn. Res., 2013)

Machine learning with operational costs. - Theja Tulabandhula; Cynthia Rudin (J. Mach. Learn. Res., 2013)

Sequential event prediction. - Benjamin Letham; Cynthia Rudin; David Madigan (Mach. Learn., 2013)

Modeling Weather Impact on a Secondary Electrical Grid. - Dingquan Wang; Rebecca J. Passonneau; Michael Collins 001; Cynthia Rudin (ANT/SEIT, 2014)

Toward a Theory of Pattern Discovery. - Jonathan H. Huggins; Cynthia Rudin (ISAIM, 2014)

Generalization Bounds for Learning with Linear and Quadratic Side Knowledge. - Theja Tulabandhula; Cynthia Rudin (ISAIM, 2014)

Robust Optimization using Machine Learning for Uncertainty Sets. - Theja Tulabandhula; Cynthia Rudin (ISAIM, 2014)

Box drawings for learning with imbalanced data. - Siong Thye Goh; Cynthia Rudin (KDD, 2014)

Algorithms for interpretable machine learning. - Cynthia Rudin (KDD, 2014)

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. - Been Kim; Cynthia Rudin; Julie A. Shah (NIPS, 2014)

A Statistical Learning Theory Framework for Supervised Pattern Discovery. - Jonathan H. Huggins; Cynthia Rudin (SDM, 2014)

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing. - Theja Tulabandhula; Cynthia Rudin (Big Data, 2014)

Methods and Models for Interpretable Linear Classification. - Berk Ustun; Cynthia Rudin (CoRR, 2014)

Preference Learning (Dagstuhl Seminar 14101). - Johannes Fürnkranz; Eyke Hüllermeier; Cynthia Rudin; Roman Slowinski; Scott Sanner (Dagstuhl Reports, 2014)

Approximating the crowd. - Seyda Ertekin; Cynthia Rudin; Haym Hirsh (Data Min. Knowl. Discov., 2014)

Learning about meetings. - Been Kim; Cynthia Rudin (Data Min. Knowl. Discov., 2014)

Analytics for Power Grid Distribution Reliability in New York City. - Cynthia Rudin; Seyda Ertekin; Rebecca J. Passonneau; Axinia Radeva; Ashish Tomar; Boyi Xie; Stanley Lewis; Mark Riddle; Debbie Pangsrivinij; Tyler H. McCormick (Interfaces, 2014)

Machine learning for science and society. - Cynthia Rudin; Kiri L. Wagstaff (Mach. Learn., 2014)

On combining machine learning with decision making. - Theja Tulabandhula; Cynthia Rudin (Mach. Learn., 2014)

Falling Rule Lists. - Fulton Wang; Cynthia Rudin (AISTATS, 2015)

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. - Tong Wang 011; Cynthia Rudin; Daniel Wagner; Rich Sevieri (Big Data, 2015)

A Bayesian Approach to Learning Scoring Systems. - Seyda Ertekin; Cynthia Rudin (Big Data, 2015)

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. - Benjamin Letham; Cynthia Rudin; Tyler H. McCormick; David Madigan (CoRR, 2015)

Regulating Greed Over Time. - Stefano Tracà; Cynthia Rudin (CoRR, 2015)

Causal Falling Rule Lists. - Fulton Wang; Cynthia Rudin (CoRR, 2015)

Learning Optimized Or's of And's. - Tong Wang 011; Cynthia Rudin (CoRR, 2015)

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (CoRR, 2015)

Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge. - Theja Tulabandhula; Cynthia Rudin (Mach. Learn., 2015)

CRAFT: ClusteR-specific Assorted Feature selecTion. - Vikas K. Garg 001; Cynthia Rudin; Tommi S. Jaakkola (AISTATS, 2016)

Bayesian Rule Sets for Interpretable Classification. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (ICDM, 2016)

Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. - Benjamin Letham; Lydia M. Letham; Cynthia Rudin (KDD, 2016)

Learning Cost-Effective Treatment Regimes using Markov Decision Processes. - Himabindu Lakkaraju; Cynthia Rudin (CoRR, 2016)

Interpretable Machine Learning Models for the Digital Clock Drawing Test. - William Souillard-Mandar; Randall Davis; Cynthia Rudin; Rhoda Au; Dana L. Penney (CoRR, 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 (J. Mach. Learn. Res., 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 (Mach. Learn., 2016)

Supersparse linear integer models for optimized medical scoring systems. - Berk Ustun; Cynthia Rudin (Mach. Learn., 2016)

Learning Cost-Effective and Interpretable Treatment Regimes. - Himabindu Lakkaraju; Cynthia Rudin (AISTATS, 2017)

Scalable Bayesian Rule Lists. - Hongyu Yang; Cynthia Rudin; Margo I. Seltzer (ICML, 2017)

Learning Certifiably Optimal Rule Lists. - Elaine Angelino; Nicholas Larus-Stone; Daniel Alabi; Margo I. Seltzer; Cynthia Rudin (KDD, 2017)

Optimized Risk Scores. - Berk Ustun; Cynthia Rudin (KDD, 2017)

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. - Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky; Tianyu Wang 008 (CoRR, 2017)

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect. - Tong Wang 011; Cynthia Rudin (CoRR, 2017)

Dimension Reduction for Robust Covariate Shift Correction. - Fulton Wang; Cynthia Rudin (CoRR, 2017)

Learning Certifiably Optimal Rule Lists for Categorical Data. - Elaine Angelino; Nicholas Larus-Stone; Daniel Alabi; Margo I. Seltzer; Cynthia Rudin (J. Mach. Learn. Res., 2017)

A Bayesian Framework for Learning Rule Sets for Interpretable Classification. - Tong Wang 011; Cynthia Rudin; Finale Doshi-Velez; Yimin Liu; Erica Klampfl; Perry MacNeille (J. Mach. Learn. Res., 2017)

Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions. - Oscar Li; Hao Liu 015; Chaofan Chen; Cynthia Rudin (AAAI, 2018)

An Optimization Approach to Learning Falling Rule Lists. - Chaofan Chen; Cynthia Rudin (AISTATS, 2018)

Direct Learning to Rank And Rerank. - Cynthia Rudin; Yining Wang (AISTATS, 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 (CVPR Workshops, 2018)

A Minimax Surrogate Loss Approach to Conditional Difference Estimation. - Siong Thye Goh; Cynthia Rudin (CoRR, 2018)

Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference. - Awa Dieng; Yameng Liu; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (CoRR, 2018)

This looks like that: deep learning for interpretable image recognition. - Chaofan Chen; Oscar Li; Alina Barnett; Jonathan Su; Cynthia Rudin (CoRR, 2018)

Bayesian Patchworks: An Approach to Case-Based Reasoning. - Ramin Moghaddass; Cynthia Rudin (CoRR, 2018)

The age of secrecy and unfairness in recidivism prediction. - Cynthia Rudin; Caroline Wang; Beau Coker (CoRR, 2018)

Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. - John Benhart; Tianlin Duan; Peter Hase; Liuyi Zhu; Cynthia Rudin (CoRR, 2018)

Please Stop Explaining Black Box Models for High Stakes Decisions. - Cynthia Rudin (CoRR, 2018)

An Interpretable Model with Globally Consistent Explanations for Credit Risk. - Chaofan Chen; Kangcheng Lin; Cynthia Rudin; Yaron Shaposhnik; Sijia Wang; Tong Wang 011 (CoRR, 2018)

Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. - Cynthia Rudin; Berk Ustun (Interfaces, 2018)

Learning customized and optimized lists of rules with mathematical programming. - Cynthia Rudin; Seyda Ertekin (Math. Program. Comput., 2018)

Systems Optimizations for Learning Certifiably Optimal Rule Lists. - Nicholas Larus-Stone; Elaine Angelino; Daniel Alabi; Margo Seltzer; Vassilios Kaxiras; Aditya Saligrama; Cynthia Rudin (SysML Conference 2018, 2018)

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

Interpretable Image Recognition with Hierarchical Prototypes. - Peter Hase; Chaofan Chen; Oscar Li; Cynthia Rudin (HCOMP, 2019)

Do Simpler Models Exist and How Can We Find Them? - Cynthia Rudin (KDD, 2019)

This Looks Like That: Deep Learning for Interpretable Image Recognition. - Chaofan Chen; Oscar Li; Daniel Tao; Alina Barnett; Cynthia Rudin; Jonathan Su (NeurIPS, 2019)

Optimal Sparse Decision Trees. - Xiyang Hu; Cynthia Rudin; Margo I. Seltzer (NeurIPS, 2019)

Interpretable Almost Matching Exactly With Instrumental Variables. - M. Usaid Awan; Yameng Liu; Marco Morucci; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (UAI, 2019)

Reducing Exploration of Dying Arms in Mortal Bandits. - Stefano Tracà; Weiyu Yan; Cynthia Rudin (UAI, 2019)

Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models. - Jiayun Dong; Cynthia Rudin (CoRR, 2019)

A Practical Bandit Method with Advantages in Neural Network Tuning. - Tianyu Wang 008; Dawei Geng; Cynthia Rudin (CoRR, 2019)

The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis. - Cynthia Rudin; David Carlson (CoRR, 2019)

A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning. - Lesia Semenova; Cynthia Rudin (CoRR, 2019)

The Big Data Newsvendor: Practical Insights from Machine Learning. - Gah-Yi Ban; Cynthia Rudin (Oper. Res., 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 (J. Mach. Learn. Res., 2019)

Learning Optimized Risk Scores. - Berk Ustun; Cynthia Rudin (J. Mach. Learn. Res., 2019)

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. - Cynthia Rudin (Nat. Mach. Intell., 2019)

A Transformer Approach to Contextual Sarcasm Detection in Twitter. - Hunter Gregory; Steven Li; Pouya Mohammadi 002; Natalie Tarn; Rachel Lea Draelos; Cynthia Rudin (Fig-Lang@ACL, 2020)

Metaphor Detection Using Contextual Word Embeddings From Transformers. - Jerry Liu; Nathan O'Hara; Alexander Rubin; Rachel Lea Draelos; Cynthia Rudin (Fig-Lang@ACL, 2020)

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. - M. Usaid Awan; Marco Morucci; Vittorio Orlandi; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (AISTATS, 2020)

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. - Sachit Menon; Alexandru Damian; Shijia Hu; Nikhil Ravi; Cynthia Rudin (CVPR, 2020)

Towards Practical Lipschitz Bandits. - Tianyu Wang 008; Weicheng Ye; Dawei Geng; Cynthia Rudin (FODS, 2020)

Generalized and Scalable Optimal Sparse Decision Trees. - Jimmy Lin; Chudi Zhong; Diane Hu; Cynthia Rudin; Margo I. Seltzer (ICML, 2020)

Bandits for BMO Functions. - Tianyu Wang 008; Cynthia Rudin (ICML, 2020)

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. - Marco Morucci; Vittorio Orlandi; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (UAI, 2020)

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction. - Caroline Wang; Bin Han; Bhrij Patel; Feroze Mohideen; Cynthia Rudin (CoRR, 2020)

Generalized Optimal Sparse Decision Trees. - Jimmy Lin; Chudi Zhong; Diane Hu; Cynthia Rudin; Margo I. Seltzer (CoRR, 2020)

Cryo-ZSSR: multiple-image super-resolution based on deep internal learning. - Qinwen Huang; Ye Zhou; Xiaochen Du; Reed Chen; Jianyou Wang; Cynthia Rudin; Alberto Bartesaghi (CoRR, 2020)

Concept whitening for interpretable image recognition. - Zhi Chen 009; Yijie Bei; Cynthia Rudin (Nat. Mach. Intell., 2020)

Exploring the cloud of variable importance for the set of all good models. - Jiayun Dong; Cynthia Rudin (Nat. Mach. Intell., 2020)

AI reflections in 2019. - Alexander S. Rich; Cynthia Rudin; David M. P. Jacoby; Robin Freeman; Oliver R. Wearn; Henry Shevlin; Kanta Dihal; Seán S. ÓhÉigeartaigh; James Butcher; Marco Lippi 001; Przemyslaw Palka; Paolo Torroni; et al. (Nat. Mach. Intell., 2020)

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 (AIES, 2021)

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference. - Neha R. Gupta; Vittorio Orlandi; Chia-Rui Chang; Tianyu Wang 008; Marco Morucci; Pritam Dey; Thomas J. Howell; Xian Sun; Angikar Ghosal; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (CoRR, 2021)

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. - Cynthia Rudin; Chaofan Chen; Zhi Chen 009; Haiyang Huang 003; Lesia Semenova; Chudi Zhong (CoRR, 2021)

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. - Alina Jade Barnett; Fides Regina Schwartz; Chaofan Tao; Chaofan Chen; Yinhao Ren; Joseph Y. Lo; Cynthia Rudin (CoRR, 2021)

Multitask Learning for Citation Purpose Classification. - Alex Oesterling; Angikar Ghosal; Haoyang Yu; Rui Xin; Yasa Baig; Lesia Semenova; Cynthia Rudin (CoRR, 2021)

Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. - Alina Jade Barnett; Fides Regina Schwartz; Chaofan Tao; Chaofan Chen; Yinhao Ren; Joseph Y. Lo; Cynthia Rudin (CoRR, 2021)

BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales. - Yunyao Zhu; Stephen Hahn; Simon Mak; Yue Jiang; Cynthia Rudin (CoRR, 2021)

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. - Zhi Chen 009; Alexander C. Ogren; Chiara Daraio; L. Catherine Brinson; Cynthia Rudin (CoRR, 2021)

Playing Codenames with Language Graphs and Word Embeddings. - Divya Koyyalagunta; Anna Y. Sun; Rachel Lea Draelos; Cynthia Rudin (J. Artif. Intell. Res., 2021)

Regulating Greed Over Time in Multi-Armed Bandits. - Stefano Tracà; Cynthia Rudin; Weiyu Yan (J. Mach. Learn. Res., 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 003; Cynthia Rudin; Yaron Shaposhnik (J. Mach. Learn. Res., 2021)

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. - Tianyu Wang 008; Marco Morucci; M. Usaid Awan; Yameng Liu; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (J. Mach. Learn. Res., 2021)

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. - Beau Coker; Cynthia Rudin; Gary King (Manag. Sci., 2021)

A case-based interpretable deep learning model for classification of mass lesions in digital mammography. - Alina Jade Barnett; Fides Regina Schwartz; Chaofan Tao; Chaofan Chen; Yinhao Ren; Joseph Y. Lo; Cynthia Rudin (Nat. Mach. Intell., 2021)

There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. - Jianyou Wang; Cynthia Rudin; Yuren Zhou; Christopher Suh; Xiaoxuan Zhang (Trans. Assoc. Comput. Linguistics, 2021)

2D elastodynamic metamaterials. - Alexander C. Ogren; Zhi Chen 009; L. Catherine Brinson; Mary Bastawrous; Cynthia Rudin; Chiara Daraio (UCI Machine Learning Repository, 2021)

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

Fast Sparse Classification for Generalized Linear and Additive Models. - Jiachang Liu 001; Chudi Zhong; Margo I. Seltzer; Cynthia Rudin (AISTATS, 2022)

Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. - Ali Behrouz; Mathias Lécuyer; Cynthia Rudin; Mango I. Seltzer (CIKM Workshops, 2022)

On the Existence of Simpler Machine Learning Models. - Lesia Semenova; Cynthia Rudin; Ronald Parr (FAccT, 2022)

Interpretable deep learning models for better clinician-AI communication in clinical mammography. - Alina Jade Barnett; Vaibhav Sharma; Neel Gajjar; Jerry Fang; Fides Regina Schwartz; Chaofan Chen; Joseph Y. Lo; Cynthia Rudin (Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 2022)

FasterRisk: Fast and Accurate Interpretable Risk Scores. - Jiachang Liu 001; Chudi Zhong; Boxuan Li; Margo I. Seltzer; Cynthia Rudin (NeurIPS, 2022)

Exploring the Whole Rashomon Set of Sparse Decision Trees. - Rui Xin; Chudi Zhong; Zhi Chen 009; Takuya Takagi; Margo I. Seltzer; Cynthia Rudin (NeurIPS, 2022)

Data poisoning attacks on off-policy policy evaluation methods. - Elita A. Lobo; Harvineet Singh; Marek Petrik; Cynthia Rudin; Himabindu Lakkaraju (UAI, 2022)

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. - Zijie J. Wang; Chudi Zhong; Rui Xin; Takuya Takagi; Zhi Chen 009; Duen Horng Chau; Cynthia Rudin; Margo I. Seltzer (IEEE VIS (Short Papers), 2022)

Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It. - Harsh Parikh; Kentaro Hoffman; Haoqi Sun; Wendong Ge; Jin Jing; Rajesh Amerineni; Lin Liu; Jimeng Sun 001; Sahar Zafar; Aaron Struck; Alexander Volfovsky; Cynthia Rudin; et al. (CoRR, 2022)

SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation. - Haiyang Huang 003; Zhi Chen 009; Cynthia Rudin (CoRR, 2022)

There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. - Yishay Mansour; Michal Moshkovitz; Cynthia Rudin (CoRR, 2022)

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. - Ali Behrouz; Mathias Lécuyer; Cynthia Rudin; Margo I. Seltzer (CoRR, 2022)

Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. - Alina Jade Barnett; Zhicheng Guo; Jin Jing; Wendong Ge; Cynthia Rudin; M. Brandon Westover (CoRR, 2022)

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. - Chaofan Chen; Kangcheng Lin; Cynthia Rudin; Yaron Shaposhnik; Sijia Wang; Tong Wang 011 (Decis. Support Syst., 2022)

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. - Tong Wang 011; Cynthia Rudin (INFORMS J. Comput., 2022)

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. - Chunxiao Li; Cynthia Rudin; Tyler H. McCormick (J. Mach. Learn. Res., 2022)

MALTS: Matching After Learning to Stretch. - Harsh Parikh; Cynthia Rudin; Alexander Volfovsky (J. Mach. Learn. Res., 2022)

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

The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. - Edwin Agnew; Michelle Qiu; Lily Zhu; Sam Wiseman; Cynthia Rudin (ACL (2), 2023)

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? - Zhi Chen 009; Sarah Tan; Urszula Chajewska; Cynthia Rudin; Rich Caruana (CHIL, 2023)

An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. - Stephen Hahn; Rico Zhu; Simon Mak; Cynthia Rudin; Yue Jiang (KDD, 2023)

A user interface to communicate interpretable AI decisions to radiologists. - Yanchen Jessie Ou; Alina Jade Barnett; Anika Mitra; Fides Regina Schwartz; Chaofan Chen; Lars J. Grimm; Joseph Y. Lo; Cynthia Rudin (Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 2023)

A user interface to communicate interpretable AI decisions to radiologists (Erratum). - Yanchen Ou; Alina Jade Barnett; Anika Mitra; Fides Regina Schwartz; Chaofan Chen; Lars J. Grimm; Joseph Y. Lo; Cynthia Rudin (Medical Imaging: Image Perception, Observer Performance, and Technology Assessment, 2023)

OKRidge: Scalable Optimal k-Sparse Ridge Regression. - Jiachang Liu 001; Sam Rosen; Chudi Zhong; Cynthia Rudin (NeurIPS, 2023)

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. - Jon Donnelly; Srikar Katta; Cynthia Rudin; Edward P. Browne (NeurIPS, 2023)

This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. - Chiyu Ma; Brandon Zhao; Chaofan Chen; 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 009; Jiachang Liu 001; Margo I. Seltzer; Cynthia Rudin (NeurIPS, 2023)

Variable importance matching for causal inference. - Quinn Lanners; Harsh Parikh; Alexander Volfovsky; Cynthia Rudin; David Page (UAI, 2023)

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. - Quinn Lanners; Harsh Parikh; Alexander Volfovsky; Cynthia Rudin; David Page (CoRR, 2023)

Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models. - Zhi Chen 009; Chudi Zhong; Margo I. Seltzer; Cynthia Rudin (CoRR, 2023)

OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems. - Jiachang Liu 001; Sam Rosen; Chudi Zhong; Cynthia Rudin (CoRR, 2023)

A Double Machine Learning Approach to Combining Experimental and Observational Data. - Marco Morucci; Vittorio Orlandi; Harsh Parikh; Sudeepa Roy; Cynthia Rudin; Alexander Volfovsky (CoRR, 2023)

A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables. - Pranay Jain; Cheng Ding; Cynthia Rudin; Xiao Hu 002 (CoRR, 2023)

Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation. - Sully F. Chen; Zhicheng Guo; Cheng Ding; Xiao Hu 002; Cynthia Rudin (CoRR, 2023)

SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection. - Zhicheng Guo; Cheng Ding; Duc H. Do; Amit J. Shah; Randall J. Lee; Xiao Hu 002; Cynthia Rudin (CoRR, 2023)

Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties. - Han Zhang; Rayehe Karimi Mahabadi; Cynthia Rudin; Johann Guilleminot; L. Catherine Brinson (CoRR, 2023)

Estimating Trustworthy and Safe Optimal Treatment Regimes. - Harsh Parikh; Quinn Lanners; Zade Akras; Sahar F. Zafar; M. Brandon Westover; Cynthia Rudin; Alexander Volfovsky (CoRR, 2023)

Fast and Interpretable Mortality Risk Scores for Critical Care Patients. - Chloe Qinyu Zhu; Muhang Tian; Lesia Semenova; Jiachang Liu 001; Jack Xu; Joseph Scarpa; Cynthia Rudin (CoRR, 2023)

Reconsideration on evaluation of machine learning models in continuous monitoring using wearables. - Cheng Ding; Zhicheng Guo; Cynthia Rudin; Ran Xiao; Fadi B. Nahab; Xiao Hu 002 (CoRR, 2023)

ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges. - Dennis Tang; Frank Willard; Ronan Tegerdine; Luke Triplett; Jon Donnelly; Luke Moffett; Lesia Semenova; Alina Jade Barnett; Jin Jing; Cynthia Rudin; M. Brandon Westover (CoRR, 2023)

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. - Cynthia Rudin; Yaron Shaposhnik (J. Mach. Learn. Res., 2023)

From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues. - Lina Zhou; Cynthia Rudin; Matthew C. Gombolay; Jim Spohrer; Michelle Zhou; Souren Paul (AIS Trans. Hum. Comput. Interact., 2023)

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 (AAAI, 2024)

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

Safe and Interpretable Estimation of Optimal Treatment Regimes. - Harsh Parikh; Quinn 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 009; Vittorio Orlandi; Tong Wang 011; Cynthia Rudin (AISTATS, 2024)

Optimal Sparse Survival Trees. - Rui Zhang; Rui Xin; Margo I. Seltzer; Cynthia Rudin (AISTATS, 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 (CVPR Workshops, 2024)

Position: Amazing Things Come From Having Many Good Models. - Cynthia Rudin; Chudi Zhong; Lesia Semenova; Margo I. Seltzer; Ronald Parr; Jiachang Liu 001; Srikar Katta; Jon Donnelly; Harry Chen; Zachery Boner (ICML, 2024)

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 (KDD, 2024)

What is different between these datasets? - Varun Babbar; Zhicheng Guo; Cynthia Rudin (CoRR, 2024)

SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals. - Cheng Ding; Zhicheng Guo; Zhaoliang Chen; Randall J. Lee; Cynthia Rudin; Xiao Hu 002 (CoRR, 2024)

This Looks Better than That: Better Interpretable Models with ProtoPNeXt. - Frank Willard; Luke Moffett; Emmanuel Mokel; Jon Donnelly; Stark Guo; Julia Yang; Giyoung Kim; Alina Jade Barnett; Cynthia Rudin (CoRR, 2024)

Amazing Things Come From Having Many Good Models. - Cynthia Rudin; Chudi Zhong; Lesia Semenova; Margo I. Seltzer; Ronald Parr; Jiachang Liu 001; Srikar Katta; Jon Donnelly; Harry Chen; Zachery Boner (CoRR, 2024)

A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis. - Stephen Ni-Hahn; Weihan Xu; Jerry Yin; Rico Zhu; Simon Mak; Yue Jiang; Cynthia Rudin (CoRR, 2024)

Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning. - Mary Bastawrous; Zhi Chen 009; Alexander C. Ogren; Chiara Daraio; Cynthia Rudin; L. Catherine Brinson (CoRR, 2024)

Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. - Manickam Ashokkumar; Wenwen Mei; Jackson J. Peterson; Yuriko Harigaya; David M. Murdoch; David M. Margolis; Caleb Kornfein; Alexander X. Oesterling; Zhicheng Guo; Cynthia Rudin; Yuchao Jiang; Edward P. Browne (Genom. Proteom. Bioinform., 2024)

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms. - Cheng Ding; Zhicheng Guo; Cynthia Rudin; Ran Xiao; Amit J. Shah; Duc H. Do; Randall J. Lee; Gari D. Clifford; Fadi B. Nahab; Xiao Hu 002 (IEEE J. Biomed. Health Informatics, 2024)