Papers & Code

2024

Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection using Eight Million Samples Labeled with Imprecise Arrhythmia Alarms. IEEE Journal of Biomedical and Health Informatics (JBHI), accepted, 2024.

Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Amit Shah, Duc H. Do, Randall J Lee, Gari Clifford, Fadi B Nahab, Xiao Hu

Evaluating Pre-trial Programs Using Machine Learning Matching Algorithms. AAAI (oral), 2024.

Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Sparse Density Trees and Lists: An Interpretable Alternative to High-Dimensional Histograms. INFORMS Journal on Data Science, 2023. (publisher's site)

Siong Thye Goh, Lesia Semenova and Cynthia Rudin

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data., AISTATS, 2024

- Joint Statistical Meeting Paper Award, American Statistical Association, Biometrics Section, 2024.
Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky.

Optimal Sparse Survival Trees. AISTATS, 2024

Rui Zhang, Rui Xin, Margo Seltzer, and Cynthia Rudin.

Sparse and Faithful Explanations without Sparse Models. AISTATS, 2024

- Winner of Data Mining Best Paper Award, INFORMS, 2023.
Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang and Cynthia Rudin.

Safe and Interpretable Estimation of Optimal Treatment Regimes. AISTATS, 2024

Harsh Parikh, Quinn Lanners, Zade Akras, Sahar Zafar, M Brandon Westover, Cynthia Rudin, and Alexander Volfovsky.

AsymMirai: Interpretable Mammography-Based Deep Learning Model for 1- to 5-year Breast Cancer Risk Prediction, Radiology, in press, 2024

Jon Donnelly, Luke Moffett, Alina Barnett, Hari Trivedi, Fides Regina Schwartz, Joseph Lo, Cynthia Rudin.

2023

Interpretable Algorithmic Forensics. Proceedings of the National Academy of Sciences (PNAS), 2023

Brandon L. Garrett and Cynthia Rudin

OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems. NeurIPS spotlight, 2023. (bib)

Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. NeurIPS spotlight, 2023. (bib)

Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models, NeurIPS, 2023. (bib) | (YouTube teaser) | (code)

Zhi Chen, Chudi Zhong, Margo Seltzer, Cynthia Rudin

A Path to Simpler Models Starts With Noise, NeurIPS, 2023. (bib)

Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin

This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations, NeurIPS, 2023. (bib)

Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin

ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges, Medical Imaging Meets NeurIPS Workshop (oral), 2023.

Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, Brandon Westover

New Orleans: An Adventure In Music, NeurIPS Creative AI Track, 2023.

Stephen Hahn, Rico Zhu, Jerry Yin, Yue Jiang, Simon Mak, Cynthia Rudin

Optimal Sparse Regression Trees, AAAI, 2023. (code) | (bib)

Rui Zhang, Rui Xin, Margo Seltzer, Cynthia Rudin

The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. 61st Annual Meeting of the Association for Computational Linguistics (ACL’23).

Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, Cynthia Rudin
- Outstanding Paper, ACL 2023.

Effects of Epileptiform Activity on Discharge Outcome in Critically Ill Patients in the USA: A Retrospective Cross-Sectional Study. The Lancet Digital Health, 2023. (arXiv version)

Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover

An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. KDD, 2023.

Stephen Hahn, Rico Zhu, Simon Mak, Cynthia Rudin, Yue Jiang.

Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. Genomics, Proteomics, and Bioinformatics, 2023.

Ashokkumar Manickam, Jackson J Peterson, Yuriko Harigaya, David M Murdoch, David M Margolis, Alex Oesterling, Zhicheng Guo, Cynthia D Rudin, Yuchao Jiang, Edward P Browne

Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation. 2023

Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin

A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables. 2023

Pranay Jain, Cheng Ding, Cynthia Rudin, Xiao Hu

Prediction of Tensile Performance for 3D Printed Photopolymer Gyroid Lattices using Structural Porosity, Base Material Properties, and Machine Learning. Materials & Design, 2023.

Jacob Peloquin, Alina Kirillova, Cynthia Rudin, L.C. Brinson, Ken Gall

Tensile Performance Data of 3D Printed Photopolymer Gyroid Lattices. Data in Brief, 2023.

Jacob Peloquin, Alina Kirillova, Elizabeth Mathey, Cynthia Rudin, L.Catherine Brinson, Ken Gall

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. Journal of Machine Learning Research, 2023. (Preliminary version appeared in CIST, 2019.) (code) | (bib)

Cynthia Rudin and Yaron Shaposhnik

From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference, UAI, 2023.

Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL, 2023.

Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana

Interpretable Prediction Rules for Congestion Risk in ICUs. Stochastic Systems, 2023. (ePrint)

Fernanda Bravo, Cynthia Rudin, Yuting Yuan and Yaron Shaposhnik

Impact of Cannabis Use on Immune Cell Populations and the Viral Reservoir in People with HIV on Suppressive Antiretroviral Therapy, The Journal of Infectious Disease (JID), 2023. (bib)

Shane D Falcinelli, Alicia Volkheimer, Lesia Semenova, Ethan Wu, Alexander Richardson, Manickam Ashokkumar, David M Margolis, Nancie M Archin, Cynthia D Rudin, David Murdoch, Edward P Browne.

Applied Machine Learning as a Driver for Polymeric Biomaterials Design, Nature Communications, 2023.

Samantha McDonald, Emily K. Augustine, Quinn Lanners, Cynthia Rudin, L. Catherine Brinson, and Matthew Becker

Fast and Interpretable Mortality Risk Scores for Critical Care Patients. 2023.

Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin.

2022

Exploring the Whole Rashomon Set of Sparse Decision Trees, NeurIPS (oral), 2022. (code) | (bib) | (5 min video)

- Finalist for INFORMS 2022 Data Mining Best Paper Competition Award, Student Track
Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo Seltzer, Cynthia Rudin

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization, IEEE VIS, 2022. (TimberTrek link)

Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo Seltzer

FasterRisk: Fast and Accurate Interpretable Risk Scores, NeurIPS, 2022. (bib) | (code)

Jiachang Liu, Chudi Zhong, Boxuan Li, Margo Seltzer, Cynthia Rudin

MALTS: Matching After Learning to Stretch, Journal of Machine Learning Research, 2022. (bib) | (code)

Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

The Importance of Being Ernest, Ekundayo, or Eswari: An Interpretable Machine Learning Approach to Name-Based Ethnicity Classification. Harvard Data Science Review, 2022. (code) | (bib)

Vaishali Jain, Ted Enamorado, and Cynthia Rudin

Towards a Comprehensive Evaluation of Dimension Reduction Methods for Transcriptomic Data Visualization. Communications Biology (Nature), 2022.

Haiyang Huang, Yingfan Wang, Cynthia Rudin, and Edward P. Browne

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. Journal of Machine Learning Research, 2022. (bib)

Chunxiao Li, Cynthia Rudin, Tyler H. McCormick

On the Existence of Simpler Machine Learning Models. ACM Conference on Fairness, Accountability, and Transparency (ACM FAccT), 2022. (bib)

Lesia Semenova, Cynthia Rudin, and Ron Parr

Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. 2022.

Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover

Fast Sparse Classification for Generalized Linear and Additive Models. AISTATS, 2022. (code) | (bib)

Jiachang Liu, Chudi Zhong, Margo Seltzer, Cynthia Rudin

Hypothesis Tests That Are Robust to Choice of Matching Method, INFORMS Journal on Data Science, 2022. (publisher's link)

Marco Morucci, Md. Noor-E-Alam, Cynthia Rudin

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. Statistics Surveys, 2022. (publisher's link) | (bib)

Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong

Fast Sparse Decision Tree Optimization via Reference Ensembles, AAAI, 2022. (video teaser) | (talk) | (code) | (bib)

Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo Seltzer

Subgroup Identification for Enhanced Treatment Effect with Decision Rules, INFORMS Journal on Computing, 2022. (publisher's link)

Tong Wang and Cynthia Rudin.

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning, Extreme Mechanics Letters, 2022

-Winner of the 2022 Physical and Engineering Sciences (SPES) and the Quality and Productivity (Q&P) Student Paper Competition of the American Statistical Association.
Zhi Chen, Alexander Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction. Journal of Quantitative Criminology, 2022. (arXiv version) | (bib) | (code)

Caroline Wang, Bin Han, Bhrij Patel, Feroze Mohideen, Cynthia Rudin

Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. UAI (oral), 2022.

Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, and Himabindu Lakkaraju

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design, Workshop on Advances in Interpretable Machine Learning and Artificial Intelligence (AIMLAI) at CIKM, 2022. (bib)

Ali Behrouz, Mathias Lecuyer, Cynthia Rudin, Margo Seltzer

Can a Computer Really Write Poetry?, Harvard Data Science Review, 2022

Edwin Agnew, Lily Zhu, Sam Wiseman, and Cynthia Rudin

Why Black Box Machine Learning Should be Avoided for High-Stakes Decisions, In Brief, (1000 words), Nature Reviews Methods Primers, 2022.

Cynthia Rudin

Moving Towards a More Equal World, One Ride at a Time: Studying Public Transportation Initiatives Using Interpretable Causal Inference, NeurIPS Workshop on Causal Machine Learning for Real-World Impact, 2022.

-Winner of the American Statistical Association Data Challenge Expo Student Competition (Student Winners), 2022
Gaurav Rajesh Parikh, Jenny Huang, Albert Sun, Lesia Semenova, Cynthia Rudin

2021

BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales, 2021

Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin

A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations, Decision Support Systems, 2021. (publisher's link) | (code)

Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang.

A Truth Serum for your Personal Perspective on Facial Recognition Software in Law Enforcement, Translational Criminology, 2021

Cynthia Rudin and Shawn Bushway

Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization, AAAI/ACM Conference on Artificial Intelligence, Ethics and Society (AIES), 2021. (video teaser)

Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan

Interpretable, not black-box, artificial intelligence should be used for embryo selection, Human Reproduction Open, 2021

Michael Anis Mihdi Afnan, Yanhe Liu, Vincent Conitzer, Cynthia Rudin, Abhishek Mishra, Julian Savulescu, Masoud Afnan

Data solidarity for machine learning for embryo selection: a call for the creation of an open access repository of embryo data, Reproductive Biomedicine Online (RBMO), 2022

Masoud Afnan, Michael Anis Mihdi Afnan, Yanhe Liu, Julian Savulescu, Abhishek Mishra, Vincent Conitzer, Cynthia Rudin

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography, Nature Machine Intelligence, 2021. (arXiv) | (bib) | (press release)

Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin

Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. IJCAI-21 Workshop on Deep Learning, Case-Based Reasoning, and AutoML: Present and Future Synergies, 2021.

Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, and Cynthia Rudin

A Supervised Machine Learning Semantic Segmentation Approach for Detecting Artifacts in Plethysmography Signals from Wearables, Physiological Measurement, 2021.

Zhicheng Guo, Cheng Ding, Xiao Hu and Cynthia Rudin

There Once Was a Really Bad Poet, It Was Automated but You Didn’t Know It. Transactions of the Association for Computational Linguistics (TACL), 2021. (code)

Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Chris Suh, and Cynthia Rudin.

Playing Codenames with Language Graphs and Word Embeddings, Journal of Artificial Intelligence Research (JAIR), 2021 (publisher's site)

Divya Koyyalagunta, Anna Sun, Rachel Lea Draelos, Cynthia Rudin

Multitask Learning for Citation Purpose Classification, Second Workshop on Scholarly Document Processing, NAACL, 2021

Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin
- One of four oral presentations on the 3C Shared Task Competition. Won third place in the competition.

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. Journal of Machine Learning Research, 2021 (code) | (bib)

Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky.

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference, 2021 (code)

Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
- Software for FLAME designed by Vittorio Orlandi and Neha Gupta was the honorable mention for the 2022 John M. Chambers Statistical Software Award from the American Statistical Association.

Regulating Greed Over Time in Multi-Armed Bandits. Journal of Machine Learning Research, 2021. (code) | (bib)

Stefano Traca, Cynthia Rudin, and Weiyu Yan
-Finalist for 2015 IBM Service Science Best Student Paper Award.
-Winner of best paper award, INFORMS 2016 Data Mining & Decision Analytics (DMDA) Workshop.

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. (bib) | (code)

Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik
- Software for PaCMAP was the winner of the 2023 John M. Chambers Statistical Software Award from the American Statistical Association.

Letter to White House: Criminal Justice AI Should Not be ‘Black Box’ or Non-Transparent.

Brandon Garrett and Cynthia Rudin

2020

Concept Whitening for Interpretable Image Recognition. Nature Machine Intelligence, 2020. (code) | (teaser video) | (arXiv version) | (bib)

Zhi Chen, Yijie Bei, Cynthia Rudin

Exploring the Cloud of Variable Importance for the Set of All Good Models, Nature Machine Intelligence, 2020. (code) | (arXiv version) | (bib)

Jiayun Dong, Cynthia Rudin

Generalized and Scalable Optimal Sparse Decision Trees. ICML, 2020. (code) | (bib)

Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo Seltzer

Bandits for BMO Functions. ICML, 2020.

Tianyu Wang and Cynthia Rudin

Towards Practical Lipschitz Bandits, Foundations of Data Science (FODS), 2020.

Tianyu Wang, Dawei Geng, Cynthia Rudin

The Age of Secrecy and Unfairness in Recidivism Prediction. Harvard Data Science Review, 2020. (links to 6 discussion articles by famous criminologists and statisticians, and by Northpointe) | (rejoinder) | (code) | (bib)

Cynthia Rudin, Caroline Wang, and Beau Coker

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. UAI, 2020 (code)

Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CVPR, 2020. (project website with code)

Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. AISTATS, 2020 (code)

Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Management Science, 2020 (publisher's link) | (bib)

Beau Coker, Cynthia Rudin, and Gary King.

2019

Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition. Harvard Data Science Review, 2019 (bib)

Cynthia Rudin and Joanna Radin

An Application of Matching After Learning To Stretch (MALTS) to the ACIC 2018 Causal Inference Challenge Data. Observational Studies, 2019. (code)

Harsh Parikh, Cynthia Rudin, Alexander Volfovsky

This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS spotlight (top 3% of papers), 2019. (code) | (bib) | (supplement)

Chaofan Chen, Oscar Li, Chaofan Tao, Alina Barnett, Jonathan Su, Cynthia Rudin

Optimal Sparse Decision Trees. NeurIPS spotlight (top 3% of papers), 2019. (3 min teaser) | (code) | (bib)

Xiyang Hu, Cynthia Rudin, and Margo Seltzer.
This is a predecessor of GOSDT (Lin et al., ICML 2020).

Interpretable Image Recognition with Hierarchical Prototypes. AAAI-HCOMP, 2019. (code)

Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin

Risk Assessment Tools Are Not A Failed 'Minority Report’. Perspectives, Law360 | July 19, 2019, 5:50 PM EDT

Sarah Desmarais, Brandon Garrett and Cynthia Rudin

Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs. 2019 INFORMS Workshop on Data Science (DS 2019) (oral)

Fernanda Bravo, Cynthia Rudin, Yaron Shaposhnik, Yuting Yuan.

Learning Optimized Risk Scores. Journal of Machine Learning Research, 2019. Shorter version at KDD 2017. (bib for JMLR version) | (bib for KDD version) | (code)

Berk Ustun and Cynthia Rudin.
-INFORMS Innovative Applications in Analytics Award, 2019 (shared with paper on ICU seizure risk).
-Winner of 2017 INFORMS Computing Society (ICS) Best Student Paper Prize.
-Runner up for Invenia Labs SEE Award 2018 - Supporting Machine Learning Research with a Positive Impact on Social, Economic, or Environmental (SEE) Challenges.

Interpretable Almost-Exact Matching With Instrumental Variables. UAI, 2019. (bib) | (code) | (1 min video)

M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky

Reducing Exploration of Dying Arms in Mortal Bandits. UAI, 2019. (bib) | (code) | (1 min video)

Stefano Traca, Cynthia Rudin, and Weiyu Yan

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and use Interpretable Models Instead, Nature Machine Intelligence, 2019. (bib) | (free arXiv version) | (publisher's version with messed up equations)

Cynthia Rudin

AI Reflections in 2019, Nature Machine Intelligence (feature) - a summary of notes on all perspectives in NMI in 2019.

Interpretable Almost-Exact Matching for Causal Inference. AISTATS, 2019. (code) | (bib)

Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, and Alexander Volfovsky

The Big Data Newsvendor: Practical Insights from Machine Learning. Operations Research, 2019. (OR official version) | (code)

Gah-Yi Ban and Cynthia Rudin.
-Winner of Best OM paper in OR, 2021, for the best operations management paper in the journal Operations Research.

The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis. INFORMS TutORial, 2019.

Cynthia Rudin and David Carlson.

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. (code) | (bib)

Aaron Fisher, Cynthia Rudin, and Francesca Dominici.

2018

Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation, 2018.

John Benhardt, Tianlin Duan, Peter Hase, Liuyi Zhu, Cynthia Rudin
- Winner of the 2018 PoetiX Literary Turing Test Award for computer-generated poetry, 2018.

Bayesian Patchworks: An Approach to Case-Based Reasoning. Draft on ArXiv, 2018.

Ramin Moghaddass and Cynthia Rudin

Learning Customized and Optimized Lists of Rules with Mathematical Programming. Mathematical Programming C (Computation), 2018. (code) | (bib)

Cynthia Rudin and Seyda Ertekin

Systems Optimizations for Learning Certifiably Optimal Rule Lists. Conference on Machine Learning and Systems (MLSys), 2018.

Nicholas Larus-Stone, Elaine Angelino, Daniel Alabi, Margo Seltzer, Vassilios Kaxiras, Aditya Saligrama, and Cynthia Rudin.

An Interpretable Model with Globally Consistent Explanations for Credit Risk. NIPS 2018 Workshop on Challenges and Opportunities for AI in Financial Services: the Impact of Fairness, Explainability, Accuracy, and Privacy, 2018. (blog post) | (longer version)

Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, and Tong Wang
- Winner of the FICO Recognition Award for the FICO Explainable Machine Learning Challenge, 2018.

Learning Certifiably Optimal Rule Lists for Categorical Data. Journal of Machine Learning Research, 2018. Shorter version published in KDD 2017 (oral). (code) | (KDD bib) | (JMLR bib) | (YouTube teaser) | (CORELS main website) | (R-binding 1 by Dirk Eddelbuettel) | (R-binding 2 by Dirk Eddelbuettel)

Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo Seltzer, and Cynthia Rudin

New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution. New Trends in Image Restoration and Enhancement Workshop and Challenges on Super-Resolution, Dehazing, and Spectral Reconstruction, NTIRE-CVPR, 2018

Yijie Bei, Alex Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin.
- CVPR-NTIRE Super-Resolution competition: co-winners for Track 1 (classic), 2018.

Extreme Dimension Reduction for Handling Covariate Shift. Draft on ArXiv, 2018

Fulton Wang and Cynthia Rudin.

A Minimax Surrogate Loss Approach to Conditional Difference Estimation. Draft on ArXiv, 2018. (code)

Siong Thye Goh and Cynthia Rudin.

Modeling Recovery Curves With Application to Prostatectomy. Biostatistics, 2018. (code) | (bib)

Fulton Wang, Tyler McCormick, Cynthia Rudin, and John Gore.
- Winner of Best Poster Competition, Statistical Learning and Data Mining Section (SLDM) of the American Statistical Association, 2014.

Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions. AAAI, 2018. (bib) | (code)

Oscar Li, Hao Liu, Chaofan Chen and Cynthia Rudin.

An Optimization Approach to Learning Falling Rule Lists. AISTATS, 2018. (bib) | (code)

Chaofan Chen and Cynthia Rudin.

Direct Learning to Rank and Rerank. AISTATS, 2018. (bib)

Cynthia Rudin and Yining Wang.
- Finalist for 2017 QSR (Quality, Reliability and Statistics) best refereed paper competition, INFORMS 2017.

Optimized Scoring Systems: Towards Trust in Machine Learning for Healthcare and Criminal Justice. INFORMS Journal on Applied Analytics, Special Issue: 2017 Daniel H. Wagner Prize for Excellence in Operations Research Practice, September-October, 2018 (bib) | (pdf of accepted version)

Cynthia Rudin and Berk Ustun.
-Finalist for Daniel H. Wagner Prize for Excellence in Operations Research, INFORMS, 2017.

Algorithms and Justice: Scrapping the ‘Black Box.’ The Crime Report (Blog), 2018.

Cynthia Rudin (with credit to Robin Smith)

2017

Association of an Electroencephalography-Based Risk Score With Seizure Probability in Hospitalized Patients. JAMA Neurology, 2017 (editorial about our work) | (bib)

Aaron F. Struck, Berk Ustun, Andres Rodriguez Ruiz, Jong Woo Lee, Suzette LaRoche, Lawrence J. Hirsch, Emily J Gilmore, Jan Vlachy, Hiba Arif Haider, Cynthia Rudin, M Brandon Westover.
-INFORMS Innovative Applications in Analytics Award, 2019 (shared with optimal scoring systems paper).

A Bayesian Framework for Learning Rule Sets for Interpretable Classification. Journal of Machine Learning Research, 2017 (bib) | (code) | (data in csv) | (data on UCI repo) | (same data on UCI repo) | (github)

Tong Wang, Cynthia Rudin, Finale Doshi, Yimin Liu, Erica Klampfl, and Perry MacNeille.

Scalable Bayesian Rule Lists. ICML 2017, Longer version on ArXiv. (bib) | (code)

Hongyu Yang, Cynthia Rudin, and Margo Seltzer
-Winner of Statistical Learning and Data Mining Student Paper Competition, American Statistical Association, 2016.
Note that this is a predecessor of the CORELS algorithms. We recommend trying CORELS and GOSDT.

Learning Cost Effective and Interpretable Treatment Regimes in the Form of Rule Lists. AISTATS, 2017. (bib) | (code)

Himabindu Lakkaraju and Cynthia Rudin
-Finalist for 2017 INFORMS Data Mining Best Paper Competition.

The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5. JAMA Psychiatry, April 2017. (NPR article) | (bib) | (editorial)

Berk Ustun, Lenard A. Adler, Cynthia Rudin, Stephen V. Faraone, Thomas J. Spencer, Patricia Berglund, Michael J. Gruber, Ronald C. Kessler.

2016

CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS, 2016. (code) | (bib)

Vikas Garg, Cynthia Rudin and Tommi Jaakkola.

Or's of And's for Interpretable Classification with Application to Context Aware Recommender Systems. ICDM, 2016.
(bib) | (code)

Tong Wang, Cynthia Rudin, Finale Doshi, Yimin Liu, Erica Klampfl, and Perry MacNeille

Prediction Uncertainty and Optimal Experimental Design for Learning Dynamical Systems. Chaos, Volume 26, Number 6, 2016. (bib)

Benjamin Letham, Portia A. Letham, Cynthia Rudin, and Edward Browne.

A Computational Model of Inhibition of HIV-1 by Interferon-Alpha. PLoS ONE, 2016. (bib)

Edward Browne, Benjamin Letham, and Cynthia Rudin.

Analytic Research Foundations for the Next-Generation Electric Grid. The National Academies Press, 2016.

John Guckenheimer, Thomas Overbye (Co-chairs), and committee: Daniel Bienstock, Anjan Bose, Terry Boston, Jeffery Dagle, Marija D. Ilic, Christopher K. Jones, Frank P. Kelly, Yannis G. Kevrekidis, Ralph D. Masiello, Juan C. Meza, Cynthia Rudin, Robert J. Thomas, and Margaret H. Wright.

Interpretable Classification Models for Recidivism Prediction. Journal of the Royal Statistical Society, 2017. (bib) | (publisher's link)

Jiaming Zeng, Berk Ustun, and Cynthia Rudin.
- This paper won the 2015 Undergraduate Statistics Research Project Competition (USRESP) sponsored by the American Statistical Association (ASA) and the Consortium for Advancement of Undergraduate Statistics Education (CAUSE).

Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. KDD, 2016. (YouTube teaser) | (link to KDD version) | (bib)

Benjamin Letham, Lydia M. Letham, and Cynthia Rudin.

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. (link)

Ramin Moghaddass, Cynthia Rudin, and David Madigan.

Supersparse Linear Integer Models for Optimized Medical Scoring Systems. Machine Learning, 2016.
(bib) | (Springer link) | (matlab code) | (python code)| (Earlier Version) | (AAAI version with Stefano Traca)

Berk Ustun and Cynthia Rudin
-Accompanies winning entry of the 2016 INFORMS Innovative Applications in Analytics Award.

Clinical Prediction Models for Sleep Apnea: The Importance of Medical History over Symptoms. Journal of Clinical Sleep Medicine, 2016. (editorial) | (bib)

Berk Ustun, Brandon Westover, Cynthia Rudin, and Matt Bianchi.

2015

Learning Classification Models of Cognitive Conditions from Subtle Behaviors in the Digital Clock Drawing Test. Machine Learning, 2015. (bib) (talk)

William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, David J. Libon, Rodney Swenson, Catherine C. Price, Melissa Lamar, Dana L. Penney.
-Accompanies winning entry of the 2016 INFORMS Innovative Applications in Analytics Award.

A Bayesian Approach to Learning Scoring Systems. Big Data, 2015. (author's pdf) | (bib)

Seyda Ertekin and Cynthia Rudin

Reactive Point Processes: A New Approach to Predicting Power Failures in Underground Electrical Systems Annals of Applied Statistics, 2015. (supplement) | (bib) | (AAAI late breaking track version)

Seyda Ertekin, Cynthia Rudin, and Tyler McCormick.

Falling Rule Lists. AISTATS, 2015. (bib) | (python code)

Fulton Wang and Cynthia Rudin
-Winner of Statistical Learning and Data Mining Student Paper Competition, American Statistical Association, 2015.
-Finalist for Data Mining Best Student Paper Award, INFORMS 2015.

Building Interpretable Classifiers with Rules using Bayesian Analysis. Annals of Applied Statistics, 2015.
(bib) | (supplement) | (python code)

Benjamin Letham, Cynthia Rudin, Tyler McCormick and David Madigan
-Winner of Data Mining Best Student Paper Award, INFORMS 2013.
-Winner of Statistical Learning and Data Mining Student Paper Competition, American Statistical Association, 2014.
Shorter versions of this have appeared in the AAAI 2013 late breaking track, and at the KDD 2014 workshop on Data Science for Social Good.
Note that this is a predecessor of the SBRL and CORELS algorithms. We recommend trying CORELS and GOSDT.

Causal Falling Rule Lists. Working Paper, 2015.

Fulton Wang and Cynthia Rudin.

Robust Testing for Causal Inference in Natural Experiments. Working Paper, 2015.

Md. Noor-E-Alam and Cynthia Rudin

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Core Sets. Big Data, 2015. (bib) | (slides)

Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri.
-This paper won second place in the “Doing Good with OR” competition at INFORMS, 2015.

Tire Changes, Fresh Air and Yellow Flags: Challenges in Predictive Analytics for Professional Racing. Big Data, 2014. (bib)

Theja Tulabandhula and Cynthia Rudin.

Robust Nonparametric Testing for Causal Inference in Natural Experiments. Working Paper, 2015.

Md. Noor-E-Alam and Cynthia Rudin

2014

On Combining Machine Learning with Decision Making. Machine Learning, 2014. (bib) | (code)

Theja Tulabandhula and Cynthia Rudin.

Generalization Bounds for Learning with Linear, Polygonal, Quadratic, and Conic Side Knowledge. Machine Learning, 2014.
(bib) | (ISAIM version)

Theja Tulabandhula and Cynthia Rudin.

Robust Optimization using Machine Learning for Uncertainty Sets. International Symposium on Artificial Intelligence and Mathematics (ISAIM), 2014. (bib) | (longer version - working paper)

Theja Tulabandhula and Cynthia Rudin.

Box Drawings for Learning with Imbalanced Data. KDD, 2014. (bib) | (matlab code)

Siong Thye Goh and Cynthia Rudin

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. NIPS, 2014. (code) | (bib)

Been Kim, Cynthia Rudin and Julie Shah

Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society.
American Statistical Association, July 2, 2014. (bib)

Cynthia Rudin, David Dunson, Rafael Irizarry, Hongkai Ji, Eric Laber, Jeffrey Leek, Tyler McCormick, Sherri Rose, Chad Schafer, Mark van der Laan, Larry Wasserman, Lingzhou Xue.

A Statistical Learning Theory Framework for Supervised Pattern Discovery. SIAM Conference on Data Mining (SDM) 2014. (bib)

Jonathan Huggins and Cynthia Rudin.

Learning About Meetings. Data Mining and Knowledge Discovery, 2014. (bib) (AAAI Late Breaking Track version)

Been Kim and Cynthia Rudin.

Approximating the Crowd. Data Mining and Knowledge Discovery, 2014. (bib) | (appendix)

Seyda Ertekin, Cynthia Rudin, Haym Hirsh.
Shorter versions of this have appeared at the NIPS Workshop on Computational Social Science and the Wisdom of Crowds (paper, bib, Haym's Slides), and Collective Intelligence (paper and bib)

The Latent State Hazard Model, with Application to Wind Turbine Reliability. Annals of Applied Statistics, 2015. (bib)

Ramin Moghaddass and Cynthia Rudin.

Analytics for Power Grid Distribution Reliability in New York City. INFORMS Journal on Applied Analytics, 2014. (bib)(Journal on Applied Analytics link)

Cynthia Rudin, Seyda Ertekin, Rebecca Passonneau, Axinia Radeva, Ashish Tomar, Boyi Xie, Stanley Lewis, Mark Riddle, Debbie Pangsrivinij, Tyler McCormick.
- Accompanies winning entry of the 2013 INFORMS Innovative Applications in Analytics Award.

Modeling Weather Impact on a Secondary Electrical Grid. International Conference on Sustainable Energy Information Technology (SEIT-2014), 2014.

Dingquan Wang, Rebecca J. Passonneau, Michael Collins, Cynthia Rudin.

2013

Growing a List. Data Mining and Knowledge Discovery, 2013. (bib) | (python code)

Benjamin Letham, Cynthia Rudin and Katherine Heller.
-Featured on Boston Public Radio (WGBH) : “A New Way To Google”

Learning Theory Analysis for Association Rules and Sequential Event Prediction. Journal of Machine Learning Research, 2013. (bib) (COLT 2011 version and its bib)

Cynthia Rudin, Benjamin Letham and David Madigan

Sequential Event Prediction. Machine Learning, 2013. (bib)

Benjamin Letham, Cynthia Rudin, and David Madigan.

Machine Learning with Operational Costs. Journal of Machine Learning Research, 2013.
(bib) | (ISAIM version) and its (bib)

Theja Tulabandhula and Cynthia Rudin.

Machine Learning for Science and Society. Machine Learning, 2013.

Cynthia Rudin and Kiri L. Wagstaff.

Learning to Detect Patterns of Crime. ECML-PKDD, 2013. (bib) | (code) | (AAAI late breaking track version and its bib) | WIRED article

Tong Wang, Cynthia Rudin, Daniel Wagner, and Rich Sevieri.
- Ideas from this paper were implemented by the NYPD (click here for link to article by Alex Chohlas-Wood and E.S. Levine) (Journal on Applied Analytics link to their article)

The Rate of Convergence of AdaBoost. Journal of Machine Learning Research, 2013. (bib) | (COLT 2011 version and its bib)

Indraneel Mukherjee, Cynthia Rudin, and Robert Schapire.
- Solved published open problem in COLT (Computational Learning Theory).

2012

Machine Learning for the New York City Power Grid. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012. (bib)

Cynthia Rudin, David Waltz, Roger N. Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome, Delfina Isaac, Arthur Kressner, Rebecca J. Passonneau, Axinia Radeva, Leon Wu.
-Spotlight Paper for the February 2012 Issue.

A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction.
Annals of Applied Statistics, 2012. (bib)

Tyler McCormick, Cynthia Rudin, David Madigan

How to Reverse-Engineer Quality Rankings. Machine Learning, 2012. (bib) | (blog)

Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas and Gloria Chou.
-Featured in Businessweek: How to Improve Product Rankings

Does AdaBoost Always Cycle? (COLT Open Problem) | (bib) | (talk)

Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire.

Teaching “Prediction: Machine Learning and Statistics.” ICML Workshop on Teaching ML, 2012. (bib)

Cynthia Rudin.

Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid.
International Conference on Software Engineering & Knowledge Engineering (SEKE), 2012. (bib)

Boyi Xie, Rebecca J. Passonneau, Haimonti Dutta, Jing-Yeu Miaw, Axinia Radeva, Ashish Tomar, and Cynthia Rudin.

2011

On Equivalence Relationships Between Classification and Ranking Algorithms. Journal of Machine Learning Research, 2011. (bib)

Seyda Ertekin and Cynthia Rudin

21st-Century Data Miners Meet 19th-Century Electrical Cables. IEEE Computer, 2011. (bib)

Cynthia Rudin, Rebecca Passonneau, Axinia Radeva, Steve Ierome, Delfina Isaac.
-One of three articles featured on the cover.

Proceedings of the 2011 INFORMS Data Mining and Health Informatics (DM-HI) Workshop

Eds. Peter Qian, Yilu Zhou, and Cynthia Rudin.

A Discrete Optimization Approach to Supervised Ranking. Working paper, 2011.

Allison Chang, Cynthia Rudin, Dimitris Bertsimas
-Finalist for Data Mining Best Student Paper Award, INFORMS 2011.
Shorter version: A Discrete Optimization Approach to Supervised Ranking. INFORMS Workshop on Data Mining and Health Informatics, 2010. (bib)

Estimation of System Reliability Using a Semiparametric Model. IEEE EnergyTech, 2011. (bib)

Leon Wu, Timothy Teravainen, Gail Kaiser, Roger Anderson, Albert Boulanger, and Cynthia Rudin.

Evaluating Machine Learning for Improving Power Grid Reliability.
ICML workshop on “Machine Learning for Global Challenges,” 2011. (bib)

Leon Wu, Gail Kaiser, Cynthia Rudin, David Waltz, Roger Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Haimonti Dutta, and Manoj Poolery.

Data Quality Assurance and Performance Measurement of Data Mining for Preventive Maintenance of Power Grid.
KDD Workshop on Data Mining for Service and Maintenance (KDD4Service), 2011. (bib)

Leon Wu, Gail Kaiser, Cynthia Rudin, Roger Anderson.

Ordered Rules for Classification: A Discrete Optimization Approach to Associative Classification. Working Paper, 2011. (bib)

Allison Chang, Cynthia Rudin, and Dimitris Bertsimas.

Treatment Effect of Repairs to an Electrical Grid: Leveraging a Machine Learned Model of Structure Vulnerability.
KDD Workshop on Data Mining Applications in Sustainability (SustKDD), 2011. (bib)

Rebecca Passonneau, Cynthia Rudin, Axinia Radeva, Ashish Tomar and Boyi Xie.

2010

A Process for Predicting Manhole Events in Manhattan. Machine Learning, 2010. (bib)

Cynthia Rudin, Rebecca Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac. WIRED | Slashdot | US News and World Report

2009

The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List. Journal of Machine Learning Research, 2009. (bib)

Cynthia Rudin.
Shorter Version: Ranking with a P-Norm Push and its (bib), COLT, 2006.

Margin-Based Ranking and an Equivalence Between AdaBoost and RankBoost. Journal of Machine Learning Research, 2009. (bib)

Cynthia Rudin and Robert E. Schapire.
Shorter Version: Margin-Based Ranking and Boosting Meet in the Middle. (with Corinna Cortes and Mehryar Mohri) COLT, 2005. (bib)

Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods.
International Conference on Computational Linguistics and Intelligent Text Processing, 2009 (bib)

Rebecca Passonneau, Cynthia Rudin, Axinia Radeva, Zhi An Liu.

Report Cards for Manholes: Eliciting Expert Feedback for a Machine Learning Task.
International Conference on Machine Learning and Applications, 2009. (bib)

Axinia Radeva, Cynthia Rudin, Rebecca Passonneau and Delfina Isaac.
-Winner of Best Poster Award

2008 and before

Visualization of Manhole and Precursor-Type Events for the Manhattan Electrical Distribution System.
Workshop on GeoVisualization of Dynamics, Movement and Change, 11th AGILE International Conference on Geographic Information Science, 2008. (bib)

Haimonti Dutta, Cynthia Rudin, Becky Passonneau, Fred Seibel, Nandini Bhardwaj, Axinia Radeva, Zhi An Liu, Steve Ierome, Delfina Isaac.

Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking.
The 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (ACL/HLT), 2008. (bib)

Ryan Roth, Owen Rambow, Nizar Habash, Mona Diab, and Cynthia Rudin.

Analysis of Boosting Algorithms using the Smooth Margin Function. Annals of Statistics, 2007. (bib)

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies.
Shorter versions:
Precise Statements of Convergence for AdaBoost and arc-gv. AMS-IMS-SIAM Joint Summer Research Conference, 2007. (bib)
Boosting Based on a Smooth Margin. COLT, 2004. (bib)

Re-Ranking Algorithms for Name Tagging. Human Language Technology conference - North American chapter of the Association for Computational Linguistics annual meeting (HLT-NAACL) Workshop on Computationally Hard Problems and Joint Inference in Speech and Language Processing, 2006. (bib)

Heng Ji, Cynthia Rudin, Ralph Grishman.

The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. Journal of Machine Learning Research, 2004. (bib)

Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire.
- Solved well-known open theoretical problem as to whether AdaBoost attains maximum margins.
Shorter version: On the Dynamics of Boosting, NIPS 2003, does not contain the main result from the JMLR paper (bib)

Stability of Learning algorithms. Notes, 2003.

Cynthia Rudin.

Equilibrium Island Arrays in Strained Solid Films. Journal of Applied Physics, 1999.

Cynthia Rudin and Brian Spencer.