Cynthia Rudin

Affiliations:
  • Duke University, USA


According to our database1, Cynthia Rudin authored at least 184 papers between 2003 and 2024.

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Bibliography

2024
Sparse and Faithful Explanations Without Sparse Models.
CoRR, 2024

Optimal Sparse Survival Trees.
CoRR, 2024

2023
From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues.
AIS Trans. Hum. Comput. Interact., March, 2023

Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation.
J. Mach. Learn. Res., 2023

Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data.
CoRR, 2023

ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges.
CoRR, 2023

Reconsideration on evaluation of machine learning models in continuous monitoring using wearables.
CoRR, 2023

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

Estimating Trustworthy and Safe Optimal Treatment Regimes.
CoRR, 2023

Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties.
CoRR, 2023

SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection.
CoRR, 2023

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

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

A Double Machine Learning Approach to Combining Experimental and Observational Data.
CoRR, 2023

OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems.
CoRR, 2023

Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models.
CoRR, 2023

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

Variable importance matching for causal inference.
Proceedings of the Uncertainty in Artificial Intelligence, 2023

Exploring and Interacting with the Set of Good Sparse Generalized Additive Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Path to Simpler Models Starts With Noise.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

OKRidge: Scalable Optimal k-Sparse Ridge Regression.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Proceedings of the Conference on Health, Inference, and Learning, 2023

The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation.
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2023

Optimal Sparse Regression Trees.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
MALTS: Matching After Learning to Stretch.
J. Mach. Learn. Res., 2022

Rethinking Nonlinear Instrumental Variable Models through Prediction Validity.
J. Mach. Learn. Res., 2022

Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects.
INFORMS J. Comput., 2022

A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations.
Decis. Support Syst., 2022

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

Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design.
CoRR, 2022

There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes.
CoRR, 2022

SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation.
CoRR, 2022

Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It.
CoRR, 2022

TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization.
Proceedings of the 2022 IEEE Visualization and Visual Analytics (VIS), 2022

Data poisoning attacks on off-policy policy evaluation methods.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

Exploring the Whole Rashomon Set of Sparse Decision Trees.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

FasterRisk: Fast and Accurate Interpretable Risk Scores.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Interpretable deep learning models for better clinician-AI communication in clinical mammography.
Proceedings of the Medical Imaging 2022: Image Perception, 2022

On the Existence of Simpler Machine Learning Models.
Proceedings of the FAccT '22: 2022 ACM Conference on Fairness, Accountability, and Transparency, Seoul, Republic of Korea, June 21, 2022

Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design.
Proceedings of the CIKM 2022 Workshops co-located with 31st ACM International Conference on Information and Knowledge Management (CIKM 2022), 2022

Fast Sparse Classification for Generalized Linear and Additive Models.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

Fast Sparse Decision Tree Optimization via Reference Ensembles.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021
2D elastodynamic metamaterials.
Dataset, November, 2021

There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It.
Trans. Assoc. Comput. Linguistics, 2021

A case-based interpretable deep learning model for classification of mass lesions in digital mammography.
Nat. Mach. Intell., 2021

A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results.
Manag. Sci., 2021

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.
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.
J. Mach. Learn. Res., 2021

Regulating Greed Over Time in Multi-Armed Bandits.
J. Mach. Learn. Res., 2021

Playing Codenames with Language Graphs and Word Embeddings.
J. Artif. Intell. Res., 2021

How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning.
CoRR, 2021

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

Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning.
CoRR, 2021

Multitask Learning for Citation Purpose Classification.
CoRR, 2021

IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography.
CoRR, 2021

Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges.
CoRR, 2021

dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference.
CoRR, 2021

Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization.
Proceedings of the AIES '21: AAAI/ACM Conference on AI, 2021

2020
AI reflections in 2019.
Nat. Mach. Intell., 2020

Exploring the cloud of variable importance for the set of all good models.
Nat. Mach. Intell., 2020

Concept whitening for interpretable image recognition.
Nat. Mach. Intell., 2020

Cryo-ZSSR: multiple-image super-resolution based on deep internal learning.
CoRR, 2020

Generalized Optimal Sparse Decision Trees.
CoRR, 2020

In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction.
CoRR, 2020

Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation.
Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Bandits for BMO Functions.
Proceedings of the 37th International Conference on Machine Learning, 2020

Generalized and Scalable Optimal Sparse Decision Trees.
Proceedings of the 37th International Conference on Machine Learning, 2020

Towards Practical Lipschitz Bandits.
Proceedings of the FODS '20: ACM-IMS Foundations of Data Science Conference, 2020

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models.
Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020

Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference.
Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

Metaphor Detection Using Contextual Word Embeddings From Transformers.
Proceedings of the Second Workshop on Figurative Language Processing, 2020

A Transformer Approach to Contextual Sarcasm Detection in Twitter.
Proceedings of the Second Workshop on Figurative Language Processing, 2020

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

Learning Optimized Risk Scores.
J. Mach. Learn. Res., 2019

All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously.
J. Mach. Learn. Res., 2019

The Big Data Newsvendor: Practical Insights from Machine Learning.
Oper. Res., 2019

A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning.
CoRR, 2019

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

Optimal Sparse Decision Trees.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

This Looks Like That: Deep Learning for Interpretable Image Recognition.
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 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 Image Recognition with Hierarchical Prototypes.
Proceedings of the Seventh AAAI Conference on Human Computation and Crowdsourcing, 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

A Minimax Surrogate Loss Approach to Conditional Difference Estimation.
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

FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference.
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.
Mach. Learn., 2016

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

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.
Mach. Learn., 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

Regulating Greed Over Time.
CoRR, 2015

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model.
CoRR, 2015

A Bayesian Approach to Learning Scoring Systems.
Big Data, 2015

Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores.
Big Data, 2015

Falling Rule Lists.
Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014
On combining machine learning with decision making.
Mach. Learn., 2014

Machine learning for science and society.
Mach. Learn., 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

Methods and Models for Interpretable Linear Classification.
CoRR, 2014

Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing.
Big Data, 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.
Mach. Learn., 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 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.
Mach. Learn., 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

On Equivalence Relationships Between Classification and Ranking Algorithms.
J. Mach. Learn. Res., 2011

Machine Learning and the Traveling Repairman
CoRR, 2011

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


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