# Cynthia Rudin

According to our database

Collaborative distances:

^{1}, Cynthia Rudin authored at least 123 papers between 2003 and 2019.Collaborative distances:

## Timeline

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## Bibliography

2019

The Big Data Newsvendor: Practical Insights from Machine Learning.

Operations Research, 2019

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

CoRR, 2019

Reducing Exploration of Dying Arms in Mortal Bandits.

CoRR, 2019

Interpretable Almost-Matching-Exactly With Instrumental Variables.

CoRR, 2019

Interpretable Image Recognition with Hierarchical Prototypes.

CoRR, 2019

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

CoRR, 2019

Optimal Sparse Decision Trees.

CoRR, 2019

A Practical Bandit Method with Advantages in Neural Network Tuning.

CoRR, 2019

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

CoRR, 2019

Reducing Exploration of Dying Arms in Mortal Bandits.

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Interpretable Almost Matching Exactly With Instrumental Variables.

Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, 2019

Do Simpler Models Exist and How Can We Find Them?

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Interpretable Almost-Exact Matching for Causal Inference.

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Systems Optimizations for Learning Certifiably Optimal Rule Lists

Proceedings of the SysML Conference 2018, February, 2018

Learning customized and optimized lists of rules with mathematical programming.

Math. Program. Comput., 2018

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

Interfaces, 2018

An Interpretable Model with Globally Consistent Explanations for Credit Risk.

CoRR, 2018

Please Stop Explaining Black Box Models for High Stakes Decisions.

CoRR, 2018

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

CoRR, 2018

The age of secrecy and unfairness in recidivism prediction.

CoRR, 2018

Bayesian Patchworks: An Approach to Case-Based Reasoning.

CoRR, 2018

This looks like that: deep learning for interpretable image recognition.

CoRR, 2018

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

CoRR, 2018

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

CoRR, 2018

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

CoRR, 2018

A Minimax Surrogate Loss Approach to Conditional Difference Estimation.

CoRR, 2018

Direct Learning to Rank and Rerank.

CoRR, 2018

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

Proceedings of the 2018 IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2018

Direct Learning to Rank And Rerank.

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

An Optimization Approach to Learning Falling Rule Lists.

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions.

Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, 2018

2017

A Bayesian Framework for Learning Rule Sets for Interpretable Classification.

J. Mach. Learn. Res., 2017

Learning Certifiably Optimal Rule Lists for Categorical Data.

J. Mach. Learn. Res., 2017

Dimension Reduction for Robust Covariate Shift Correction.

CoRR, 2017

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect.

CoRR, 2017

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

CoRR, 2017

An optimization approach to learning falling rule lists.

CoRR, 2017

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

CoRR, 2017

Learning Certifiably Optimal Rule Lists for Categorical Data.

CoRR, 2017

Optimized Risk Scores.

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Learning Certifiably Optimal Rule Lists.

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Scalable Bayesian Rule Lists.

Proceedings of the 34th International Conference on Machine Learning, 2017

Learning Cost-Effective and Interpretable Treatment Regimes.

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

Supersparse linear integer models for optimized medical scoring systems.

Machine Learning, 2016

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

Machine Learning, 2016

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

J. Mach. Learn. Res., 2016

Scalable Bayesian Rule Lists.

CoRR, 2016

Interpretable Machine Learning Models for the Digital Clock Drawing Test.

CoRR, 2016

Learning Cost-Effective Treatment Regimes using Markov Decision Processes.

CoRR, 2016

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

Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2016

Bayesian Rule Sets for Interpretable Classification.

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

CRAFT: ClusteR-specific Assorted Feature selecTion.

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

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

Machine Learning, 2015

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

CoRR, 2015

Learning Optimized Or's of And's.

CoRR, 2015

Causal Falling Rule Lists.

CoRR, 2015

Supersparse Linear Integer Models for Optimized Medical Scoring Systems.

CoRR, 2015

Regulating Greed Over Time.

CoRR, 2015

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

CoRR, 2015

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

CoRR, 2015

CRAFT: ClusteR-specific Assorted Feature selecTion.

CoRR, 2015

Falling Rule Lists.

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

On combining machine learning with decision making.

Machine Learning, 2014

Machine learning for science and society.

Machine Learning, 2014

Analytics for Power Grid Distribution Reliability in New York City.

Interfaces, 2014

Learning about meetings.

Data Min. Knowl. Discov., 2014

Approximating the crowd.

Data Min. Knowl. Discov., 2014

Preference Learning (Dagstuhl Seminar 14101).

Dagstuhl Reports, 2014

Falling Rule Lists.

CoRR, 2014

Methods and Models for Interpretable Linear Classification.

CoRR, 2014

Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge.

CoRR, 2014

Robust Optimization using Machine Learning for Uncertainty Sets.

CoRR, 2014

Box Drawings for Learning with Imbalanced Data.

CoRR, 2014

A Statistical Learning Theory Framework for Supervised Pattern Discovery.

Proceedings of the 2014 SIAM International Conference on Data Mining, 2014

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

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Algorithms for interpretable machine learning.

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Box drawings for learning with imbalanced data.

Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2014

Robust Optimization using Machine Learning for Uncertainty Sets.

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2014

Generalization Bounds for Learning with Linear and Quadratic Side Knowledge.

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2014

Toward a Theory of Pattern Discovery.

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2014

Modeling Weather Impact on a Secondary Electrical Grid.

Proceedings of the 5th International Conference on Ambient Systems, 2014

2013

Sequential event prediction.

Machine Learning, 2013

Machine learning with operational costs.

J. Mach. Learn. Res., 2013

Learning theory analysis for association rules and sequential event prediction.

J. Mach. Learn. Res., 2013

The rate of convergence of AdaBoost.

J. Mach. Learn. Res., 2013

Growing a list.

Data Min. Knowl. Discov., 2013

Learning About Meetings.

CoRR, 2013

Toward a Theory of Pattern Discovery.

CoRR, 2013

Learning to Detect Patterns of Crime.

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2013

Detecting Patterns of Crime with Series Finder.

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

Supersparse Linear Integer Models for Predictive Scoring Systems.

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis.

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

Machine Learning for Meeting Analysis.

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

Predicting Power Failures with Reactive Point Processes.

Proceedings of the Late-Breaking Developments in the Field of Artificial Intelligence, 2013

2012

Machine Learning for theNew York City Power Grid.

IEEE Trans. Pattern Anal. Mach. Intell., 2012

How to reverse-engineer quality rankings.

Machine Learning, 2012

Open Problem: Does AdaBoost Always Cycle?

Proceedings of the COLT 2012, 2012

Learning to Predict the Wisdom of Crowds

CoRR, 2012

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

Proceedings of the 24th International Conference on Software Engineering & Knowledge Engineering (SEKE'2012), 2012

An Integer Optimization Approach to Associative Classification.

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

The Influence of Operational Cost on Estimation.

Proceedings of the International Symposium on Artificial Intelligence and Mathematics, 2012

Selective Sampling of Labelers for Approximating the Crowd.

Proceedings of the Machine Aggregation of Human Judgment, 2012

2011

Sequential Event Prediction with Association Rules.

Proceedings of the COLT 2011, 2011

The Rate of Convergence of Adaboost.

Proceedings of the COLT 2011, 2011

On Equivalence Relationships Between Classification and Ranking Algorithms.

J. Mach. Learn. Res., 2011

Machine Learning with Operational Costs

CoRR, 2011

The Rate of Convergence of AdaBoost

CoRR, 2011

Machine Learning and the Traveling Repairman

CoRR, 2011

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

IEEE Computer, 2011

The Machine Learning and Traveling Repairman Problem.

Proceedings of the Algorithmic Decision Theory - Second International Conference, 2011

2010

A process for predicting manhole events in Manhattan.

Machine Learning, 2010

2009

Margin-based Ranking and an Equivalence between AdaBoost and RankBoost.

J. Mach. Learn. Res., 2009

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

J. Mach. Learn. Res., 2009

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

Proceedings of the International Conference on Machine Learning and Applications, 2009

Online coordinate boosting.

Proceedings of the 12th IEEE International Conference on Computer Vision Workshops, 2009

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

Proceedings of the Computational Linguistics and Intelligent Text Processing, 2009

2008

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

Proceedings of the ACL 2008, 2008

2006

Ranking with a P-Norm Push.

Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005

Stability Analysis for Regularized Least Squares Regression

CoRR, 2005

Margin-Based Ranking Meets Boosting in the Middle.

Proceedings of the Learning Theory, 18th Annual Conference on Learning Theory, 2005

2004

The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins.

J. Mach. Learn. Res., 2004

Boosting Based on a Smooth Margin.

Proceedings of the Learning Theory, 17th Annual Conference on Learning Theory, 2004

2003

On the Dynamics of Boosting.

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003