Cynthia Rudin

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Re-Ranking Algorithms for Name Tagging

Heng Ji, Ralph Grishman, Cynthia Rudin, 2006 .

Methods and Models for Interpretable Linear Classification pdf

Cynthia Rudin, Berk Ustun, C. Rudin, 2014, ArXiv.

Teaching \Prediction: Machine Learning and Statistics"

Cynthia Rudin, C. Rudin, 2012 .

ORC: Ordered Rules for ClassificationA Discrete Optimization Approach to Associative Classification

Cynthia Rudin, Dimitris Bertsimas, Allison Chang, 2012 .

Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods

Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, 2009, CICLing.

On Equivalence Relationships Between Classification and Ranking Algorithms

Cynthia Rudin, Seyda Ertekin, 2011, J. Mach. Learn. Res..

How to reverse-engineer quality rankings

Cynthia Rudin, Allison Chang, Michael Cavaretta, 2012, Machine Learning.

Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference pdf

Cynthia Rudin, Sudeepa Roy, Awa Dieng, 2018, ArXiv.

Discovery with Data: Leveraging Statistics with Computer Science to Transform Science and Society

Cynthia Rudin, Rafael A. Irizarry, Eric B. Laber, 2014 .

Data quality assurance and performance measurement of data mining for preventive maintenance of power grid

Cynthia Rudin, Leon Wu, Gail Kaiser, 2011, KDD4Service '11.

Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation

Cynthia Rudin, Himabindu Lakkaraju, 2016 .

Supersparse Linear Integer Models for Interpretable Classification

Cynthia Rudin, Berk Ustun, Stefano Traca, 2013, 1306.6677.

Foundations of Sequential Learning

Kamesh Munagala, Cynthia Rudin, Ronald Parr, 2018 .

Learning customized and optimized lists of rules with mathematical programming

Cynthia Rudin, Seyda Ertekin, 2018, Math. Program. Comput..

21st-Century Data Miners Meet 19th-Century Electrical Cables

Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, 2011, Computer.

Interpretable classification models for recidivism prediction

Cynthia Rudin, Berk Ustun, Jiaming Zeng, 2015, 1503.07810.

A Minimax Surrogate Loss Approach to Conditional Difference Estimation pdf

Cynthia Rudin, Siong Thye Goh, C. Rudin, 2018, ArXiv.

Online coordinate boosting

Cynthia Rudin, Michael Jones, Raphael Pelossof, 2008, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

The Big Data Newsvendor: Practical Insights from Machine Learning

Cynthia Rudin, Gah-Yi Ban, C. Rudin, 2013, Oper. Res..

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect pdf

Tong Wang, Cynthia Rudin, C. Rudin, 2017, INFORMS J. Comput..

Almost-Exact Matching with Replacement for Causal Inference

Cynthia Rudin, Sudeepa Roy, Awa Dieng, 2018, 1806.06802.

Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts

Cynthia Rudin, Benjamin Letham, Lydia M. Letham, 2015, KDD.

A Transformer Approach to Contextual Sarcasm Detection in Twitter

Cynthia Rudin, Pouya Mohammadi, Hunter Gregory, 2020, FIGLANG.

Algorithms for interpretable machine learning

Cynthia Rudin, C. Rudin, 2014, KDD.

Bandits for BMO Functions

Cynthia Rudin, Tianyu Wang, C. Rudin, 2020, ICML.

An Integer Optimization Approach to Associative Classification

Cynthia Rudin, Dimitris Bertsimas, Allison Chang, 2012, NIPS.

Approximating the crowd

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2014, Data Mining and Knowledge Discovery.

Learning Cost-Effective and Interpretable Treatment Regimes

Cynthia Rudin, Himabindu Lakkaraju, C. Rudin, 2017, AISTATS.

Learning to Detect Patterns of Crime

Tong Wang, Cynthia Rudin, Daniel Wagner, 2013, ECML/PKDD.

Interpretable Matching for Causal Inference [R package FLAME version 2.0.0]

Cynthia Rudin, Sudeepa Roy, Alexander Volfovsky, 2020 .

A Shared Vision for Machine Learning in Neuroscience

Guillermo Sapiro, Cynthia Rudin, Kafui Dzirasa, 2018, The Journal of Neuroscience.

Evaluating Machine Learning for Improving Power Grid Reliability

Haimonti Dutta, Gail E. Kaiser, David L. Waltz, 2011 .

Please Stop Explaining Black Box Models for High Stakes Decisions pdf

Cynthia Rudin, C. Rudin, 2018, ArXiv.

Modeling Weather Impact on a Secondary Electrical Grid

Cynthia Rudin, Rebecca J. Passonneau, Michael Collins, 2014, ANT/SEIT.

Ranking with a P-Norm Push

Cynthia Rudin, C. Rudin, 2006, COLT.

A Practical Bandit Method with Advantages in Neural Network Tuning pdf

Cynthia Rudin, Tianyu Wang, Dawei Geng, 2019, ArXiv.

Robust Optimization using Machine Learning for Uncertainty Sets pdf

Cynthia Rudin, Theja Tulabandhula, C. Rudin, 2014, ISAIM.

Interpretable Almost Matching Exactly With Instrumental Variables

Cynthia Rudin, Sudeepa Roy, Yameng Liu, 2019, UAI.

Generalization Bounds for Learning with Linear and Quadratic Side Knowledge

Cynthia Rudin, Theja Tulabandhula, C. Rudin, 2014, ISAIM.

Learning theory analysis for association rules and sequential event prediction

Cynthia Rudin, David Madigan, Benjamin Letham, 2013, J. Mach. Learn. Res..

Predicting Power Failures with Reactive Point Processes

Cynthia Rudin, Seyda Ertekin, Tyler H. McCormick, 2013, AAAI.

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost

Cynthia Rudin, Robert E. Schapire, R. Schapire, 2009, J. Mach. Learn. Res..

An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis

Cynthia Rudin, David Madigan, Benjamin Letham, 2013, AAAI.

Selective Sampling of Labelers for Approximating the Crowd

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2012, AAAI Fall Symposium: Machine Aggregation of Human Judgment.

On combining machine learning with decision making

Cynthia Rudin, Theja Tulabandhula, C. Rudin, 2011, Machine Learning.

An optimization approach to learning falling rule lists

Cynthia Rudin, Chaofan Chen, C. Rudin, 2017, AISTATS.

Optimal Sparse Decision Trees pdf

Margo I. Seltzer, Cynthia Rudin, Xiyang Hu, 2019, NeurIPS.

Learning to Predict the Wisdom of Crowds pdf

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2012, ArXiv.

Machine Learning for Meeting Analysis

Cynthia Rudin, Been Kim, C. Rudin, 2013, AAAI.

Stability Analysis for Regularized Least Squares Regression pdf

Cynthia Rudin, C. Rudin, 2005, ArXiv.

An Interpretable Model with Globally Consistent Explanations for Credit Risk pdf

Cynthia Rudin, Chaofan Chen, Tong Wang, 2018, ArXiv.

Interpretable Almost-Exact Matching for Causal Inference

Cynthia Rudin, Sudeepa Roy, Awa Dieng, 2019, AISTATS.

Learning Certifiably Optimal Rule Lists

Margo I. Seltzer, Cynthia Rudin, Daniel Alabi, 2017, KDD.

Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking

Nizar Habash, Cynthia Rudin, Owen Rambow, 2008, ACL.

Bayesian Patchworks: An Approach to Case-Based Reasoning pdf

Cynthia Rudin, Ramin Moghaddass, C. Rudin, 2018, ArXiv.

Reducing Exploration of Dying Arms in Mortal Bandits

Cynthia Rudin, Stefano Traca, Weiyu Yan, 2019, UAI.

Analytics for Power Grid Distribution Reliability in New York City

Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, 2014, Interfaces.

Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice

Cynthia Rudin, Berk Ustun, C. Rudin, 2018, Interfaces.

Growing a list

Katherine A. Heller, Cynthia Rudin, Benjamin Letham, 2013, Data Mining and Knowledge Discovery.

Cryo-ZSSR: multiple-image super-resolution based on deep internal learning pdf

Cynthia Rudin, Xiaochen Du, Jianyou Wang, 2020, ArXiv.

Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models pdf

Cynthia Rudin, Jiayun Dong, C. Rudin, 2019, ArXiv.

A Bayesian Approach to Learning Scoring Systems

Cynthia Rudin, Şeyda Ertekin, C. Rudin, 2015, Big Data.

Supersparse Linear Integer Models for Predictive Scoring Systems

Cynthia Rudin, Berk Ustun, Stefano Tracà, 2013, AAAI.

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation pdf

Cynthia Rudin, Sudeepa Roy, Alexander Volfovsky, 2020, UAI.

Latent Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

Cynthia Rudin, Been Kim, Julie A. Shah, 2014 .

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction pdf

Cynthia Rudin, Bin Han, Caroline Wang, 2020, Journal of Quantitative Criminology.

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation

Cynthia Rudin, Yaron Shaposhnik, C. Rudin, 2019, J. Mach. Learn. Res..

Box drawings for learning with imbalanced data

Cynthia Rudin, Siong Thye Goh, C. Rudin, 2014, KDD.

Margin-Based Ranking Meets Boosting in the Middle

Cynthia Rudin, Mehryar Mohri, Robert E. Schapire, 2005, COLT.

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference pdf

Cynthia Rudin, Xian Sun, Sudeepa Roy, 2021, ArXiv.

This Looks Like That: Deep Learning for Interpretable Image Recognition

Cynthia Rudin, Jonathan Su, Chaofan Chen, 2018 .

Learning Optimized Risk Scores

Cynthia Rudin, Berk Ustun, C. Rudin, 2016, J. Mach. Learn. Res..

Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions

Cynthia Rudin, Chaofan Chen, Hao Liu, 2017, AAAI.

The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification

Cynthia Rudin, Been Kim, Julie A. Shah, 2014, NIPS.

The Rate of Convergence of Adaboost

Cynthia Rudin, Robert E. Schapire, Indraneel Mukherjee, 2011, COLT.

Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead

Cynthia Rudin, C. Rudin, 2018, Nature Machine Intelligence.

Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation pdf

Cynthia Rudin, John Benhart, Tianlin Duan, 2018, ArXiv.

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model pdf

Cynthia Rudin, David Madigan, Benjamin Letham, 2015, ArXiv.

Preference Learning (Dagstuhl Seminar 14101)

Eyke Hüllermeier, Johannes Fürnkranz, Scott Sanner, 2014, Dagstuhl Reports.

Generalized Optimal Sparse Decision Trees pdf

Cynthia Rudin, Jimmy Lin, Margo Seltzer, 2020, ArXiv.

Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions

Cynthia Rudin, David Madigan, Tyler H. McCormick, 2012 .

Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test

Cynthia Rudin, Catherine C. Price, Randall Davis, 2015, Machine Learning.

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously

Cynthia Rudin, Francesca Dominici, Aaron Fisher, 2019, J. Mach. Learn. Res..

Learning about meetings

Cynthia Rudin, Been Kim, C. Rudin, 2013, Data Mining and Knowledge Discovery.

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

Cynthia Rudin, Alexandru Damian, Nikhil Ravi, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

Wisely Using a Budget for Crowdsourcing

Cynthia Rudin, Haym Hirsh, Seyda Ertekin, 2012 .

Machine Learning for the New York City Power Grid

Haimonti Dutta, David L. Waltz, Cynthia Rudin, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes

Cynthia Rudin, David Madigan, Ramin Moghaddass, 2016, J. Mach. Learn. Res..

Hypothesis Tests That Are Robust to Choice of Matching Method

Md. Noor-E-Alam, Cynthia Rudin, Marco Morucci, 2018 .

CRAFT: ClusteR-specific Assorted Feature selecTion pdf

Tommi S. Jaakkola, Cynthia Rudin, Vikas K. Garg, 2015, AISTATS.

Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge

Cynthia Rudin, Theja Tulabandhula, C. Rudin, 2014, Machine Learning.

A Bayesian Framework for Learning Rule Sets for Interpretable Classification

Cynthia Rudin, Finale Doshi-Velez, Tong Wang, 2017, J. Mach. Learn. Res..

Detecting Patterns of Crime with Series Finder

Tong Wang, Cynthia Rudin, Daniel Wagner, 2013, AAAI.

Machine learning with operational costs pdf

Cynthia Rudin, Theja Tulabandhula, C. Rudin, 2011, J. Mach. Learn. Res..

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores

Tong Wang, Cynthia Rudin, Rich Sevieri, 2015, Big Data.

Learning Optimized Risk Scores on Large-Scale Datasets

Cynthia Rudin, Berk Ustun, 2016 .

The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List

Cynthia Rudin, C. Rudin, 2009, J. Mach. Learn. Res..

Matching Bounds: How Choice of Matching AlgorithmImpacts Treatment Effects Estimates and What to Do about It

Cynthia Rudin, Marco Morucci, 2020 .

A process for predicting manhole events in Manhattan

Haimonti Dutta, Axinia Radeva, Cynthia Rudin, 2009, Machine Learning.

Why Are We Using Black Box Models in AI When We Don’t Need To? A Lesson From An Explainable AI Competition

Cynthia Rudin, Joanna Radin, C. Rudin, 2019, 1.2.

The Influence of Operational Cost on Estimation

Cynthia Rudin, Theja Tulabandhula, 2012, ISAIM.

Estimation of system reliability using a semiparametric model

Cynthia Rudin, Leon Wu, Roger Anderson, 2011, IEEE 2011 EnergyTech.

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference pdf

Cynthia Rudin, Tianyu Wang, Sudeepa Roy, 2017, J. Mach. Learn. Res..

Machine Learning Algorithms for Classification

Cynthia Rudin, Robert E. Schapire, R. Schapire, 2007 .

Sequential event prediction

Cynthia Rudin, David Madigan, Benjamin Letham, 2013, Machine Learning.

New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution

Cynthia Rudin, Alexandru Damian, Nikhil Ravi, 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing

Cynthia Rudin, Theja Tulabandhula, C. Rudin, 2014, Big Data.

Boosting Based on a Smooth Margin

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, 2004, COLT.

Model Class Reliance: Variable Importance Measures for any Machine Learning Model Class, from the "Rashomon" Perspective

Cynthia Rudin, Francesca Dominici, Aaron J. Fisher, 2018 .

The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, 2004, J. Mach. Learn. Res..

Interpretable Machine Learning Models for the Digital Clock Drawing Test pdf

Cynthia Rudin, Randall Davis, Dana L. Penney, 2016, ArXiv.

Dimension Reduction for Robust Covariate Shift Correction pdf

Cynthia Rudin, Fulton Wang, C. Rudin, 2017, ArXiv.

Causal Falling Rule Lists pdf

Cynthia Rudin, Fulton Wang, 2015, ArXiv.

Bayesian Rule Sets for Interpretable Classification

Cynthia Rudin, Finale Doshi-Velez, Tong Wang, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

A Statistical Learning Theory Framework for Supervised Pattern Discovery

Cynthia Rudin, Jonathan H. Huggins, C. Rudin, 2013, SDM.

Supersparse linear integer models for optimized medical scoring systems

Cynthia Rudin, Berk Ustun, C. Rudin, 2015, Machine Learning.

Direct Learning to Rank and Rerank

Cynthia Rudin, Yining Wang, Yining Wang, 2018, AISTATS.

On the Dynamics of Boosting

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, 2003, NIPS.

Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid

Haimonti Dutta, Axinia Radeva, Cynthia Rudin, 2012, SEKE.

Failure Analysis of the New York City Power Grid

Gail E. Kaiser, Cynthia Rudin, Roger N. Anderson, 2014 .

The Machine Learning and Traveling Repairman Problem

Patrick Jaillet, Cynthia Rudin, Theja Tulabandhula, 2011, ADT.

Do Simpler Models Exist and How Can We Find Them?

Cynthia Rudin, 2019, KDD.

The age of secrecy and unfairness in recidivism prediction pdf

Cynthia Rudin, Beau Coker, Caroline Wang, 2018, 2.1.

Cascaded High Dimensional Histograms: An Approach to Interpretable Density Estimation for Categorical Data

Cynthia Rudin, Siong Thye Goh, C. Rudin, 2015 .

Scalable Bayesian Rule Lists

Margo I. Seltzer, Cynthia Rudin, Hongyu Yang, 2016, ICML.

Learning Optimized Or's of And's pdf

Tong Wang, Cynthia Rudin, C. Rudin, 2015, ArXiv.

Optimized Risk Scores

Cynthia Rudin, Berk Ustun, C. Rudin, 2017, KDD.

Interpretable Image Recognition with Hierarchical Prototypes pdf

Cynthia Rudin, Chaofan Chen, Oscar Li, 2019, HCOMP.

Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization pdf

Cynthia Rudin, Haiyang Huang, Yingfan Wang, 2020, ArXiv.

Toward a Theory of Pattern Discovery pdf

Cynthia Rudin, Jonathan H. Huggins, C. Rudin, 2014, ISAIM.

Modeling recovery curves with application to prostatectomy.

Cynthia Rudin, John L. Gore, Fulton Wang, 2015, Biostatistics.

Simple Rules for Predicting Congestion Risk in Queueing Systems: Application to ICUs

Cynthia Rudin, Fernanda Bravo, Yaron Shaposhnik, 2019, SSRN Electronic Journal.

The Big Data Newsvendor: Practical Insights from Machine Learning Analysis

Cynthia Rudin, GahhYi Vahn, C. Rudin, 2013 .

Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task

Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, 2009, 2009 International Conference on Machine Learning and Applications.

The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis pdf

Cynthia Rudin, David Carlson, C. Rudin, 2019, Operations Research & Management Science in the Age of Analytics.

Open Problem: Does AdaBoost Always Cycle?

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, 2012, COLT.

Exploring the cloud of variable importance for the set of all good models

Cynthia Rudin, Jiayun Dong, C. Rudin, 2020, Nature Machine Intelligence.

Bayesian Hierarchical Rule Modeling for Predicting Medical Conditions

Cynthia Rudin, David Madigan, Tyler H. McCormick, 2012, 1206.6653.

A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning pdf

Cynthia Rudin, Lesia Semenova, C. Rudin, 2019, ArXiv.

Regulating Greed Over Time pdf

Cynthia Rudin, Stefano Tracà, C. Rudin, 2015, ArXiv.

Learning Certifiably Optimal Rule Lists

Margo I. Seltzer, Cynthia Rudin, Daniel Alabi, 2017, KDD.

Sequential Event Prediction with Association Rules

Cynthia Rudin, David Madigan, Benjamin Letham, 2011, COLT.

Metaphor Detection Using Contextual Word Embeddings From Transformers

Cynthia Rudin, Jerry Liu, Rachel Lea Draelos, 2020, Fig-Lang@ACL.

Estimation of System Reliability Using a

Gail E. Kaiser, Cynthia Rudin, Roger N. Anderson, 2011 .

Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems pdf

Yimin Liu, Tong Wang, Cynthia Rudin, 2015, ArXiv.

MALTS: Matching After Learning to Stretch

Cynthia Rudin, Alexander Volfovsky, Harsh Parikh, 2018, J. Mach. Learn. Res..

A Learning Theory Framework for Sequential Event Prediction and Association Rules

Cynthia Rudin, David Madigan, Benjamin Letham, 2012 .

USING THE DIGITAL CLOCK DRAWING TEST AND MACHINE LEARNING TO IMPROVE ACCURACY OF COGNITIVE SCREENING

Cynthia Rudin, Randall Davis, Dana L. Penney, 2016, Alzheimer's & Dementia.

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference

Cynthia Rudin, Sudeepa Roy, Alexander Volfovsky, 2020, AISTATS.

Falling Rule Lists pdf

Cynthia Rudin, Fulton Wang, C. Rudin, 2014, AISTATS.

Learning Cost-Effective Treatment Regimes using Markov Decision Processes pdf

Cynthia Rudin, Himabindu Lakkaraju, C. Rudin, 2016, ArXiv.

Concept Whitening for Interpretable Image Recognition pdf

Cynthia Rudin, Zhi Chen, Yijie Bei, 2020, Nat. Mach. Intell..

Machine learning for science and society

Cynthia Rudin, Kiri Wagstaff, C. Rudin, 2013, Machine Learning.

Generalized and Scalable Optimal Sparse Decision Trees

Jimmy J. Lin, Margo I. Seltzer, Cynthia Rudin, 2020, ICML.

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results pdf

Cynthia Rudin, Gary King, Beau Coker, 2018, Manag. Sci..

Towards Practical Lipschitz Bandits

Cynthia Rudin, Tianyu Wang, Dawei Geng, 2020, FODS.

A case-based interpretable deep learning model for classification of mass lesions in digital mammography pdf

Joseph Y. Lo, Cynthia Rudin, Yinhao Ren, 2021, Nature Machine Intelligence.

Playing Codenames with Language Graphs and Word Embeddings

Cynthia Rudin, Rachel Lea Draelos, Divya Koyyalagunta, 2021, J. Artif. Intell. Res..

Multitask Learning for Citation Purpose Classification pdf

Haoyang Yu, Cynthia Rudin, Angikar Ghosal, 2021, ArXiv.

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning pdf

Cynthia Rudin, L. Catherine Brinson, Chiara Daraio, 2021, ArXiv.

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges pdf

Cynthia Rudin, Chaofan Chen, Chudi Zhong, 2021, ArXiv.

Regulating Greed Over Time in Multi-Armed Bandits

Cynthia Rudin, Stefano Traca, Weiyu Yan, 2021, J. Mach. Learn. Res..

Fast Sparse Decision Tree Optimization via Reference Ensembles pdf

Cynthia Rudin, Margo Seltzer, Reto Achermann, 2021, ArXiv.

A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations pdf

Cynthia Rudin, Tong Wang, Chaofan Chen, 2021, Decision Support Systems.

Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning pdf

Joseph Y. Lo, Cynthia Rudin, Yinhao Ren, 2021, ArXiv.

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

Cynthia Rudin, Simon Mak, Yunyao Zhu, 2021, ArXiv.

Prediction uncertainty and optimal experimental design for learning dynamical systems.

Cynthia Rudin, Benjamin Letham, C. Rudin, 2015, Chaos.

A Different Type of Convergence for Statistical Learning Algorithms

Cynthia Rudin, C. Rudin, 2003 .

Integer Optimization Methods for Supervised Ranking

Cynthia Rudin, Dimitris Bertsimas, Allison Chang, 2011 .

Report Cards for Manholes

Axinia Radeva, Cynthia Rudin, Rebecca Passonneau, 2009 .

Predicting Vulnerability to Serious Manhole Events In Manhattan : A Preliminary Machine Learning Approach

Haimonti Dutta, Axinia Radeva, Cynthia Rudin, 2008 .

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction pdf

Cynthia Rudin, Bhrij Patel, Feroze Mohideen, 2020, ArXiv.

A Learning Theory Framework for Association Rules and Sequential Events

Cynthia Rudin, David Madigan, Benjamin Letham, 2011 .

The latent state hazard model, with application to wind turbine reliability

Cynthia Rudin, Ramin Moghaddass, 2015 .

Interpretable Almost-Exact Matching for Causal Inference Supplementary Material

Cynthia Rudin, Sudeepa Roy, Alexander Volfovsky, 2019 .

Visualization of Manhole and Precursor-Type Events for the Manhattan Electrical Distribution System

Haimonti Dutta, Axinia Radeva, Cynthia Rudin, 2008 .

Cascaded High Dimensional Histograms: A Generative Approach to Density Estimation

Cynthia Rudin, Siong Thye Goh, C. Rudin, 2015, 1510.06779.

Robust Testing for Causal Inference in Observational Studies

Md. Noor-E-Alam, Cynthia Rudin, C. Rudin, 2015 .

Precise Statements of Convergence for AdaBoost and arc-gv

Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies, 2007 .

A Hierarchical Model for Association Rule Mining of Sequential Events: An Approach to Automated Medical Symptom Prediction

Cynthia Rudin, David Madigan, Tyler H. McCormick, 2011 .

A Practical Risk Score for EEG Seizures in Hospitalized Patients (S11.002)

Cynthia Rudin, Aaron F. Struck, Lawrence J. Hirsch, 2018 .

Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions

Cynthia Rudin, Himabindu Lakkaraju, C. Rudin, 2016 .

The World Health Organization Adult Attention-Deficit/Hyperactivity Disorder Self-Report Screening Scale for DSM-5

Cynthia Rudin, Ronald C. Kessler, Stephen V. Faraone, 2017, JAMA psychiatry.