Cynthia Rudin
81 papers · 2004–2026 · 12 venues · across top CS/AI venues
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Achievements
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πΊοΈ Taxonomy Completionist (84) π Academic Marathon Β· Ultra π Conference Polyglot Β· Gold π Interdisciplinary Bridge Β· Silver π£ Hot Topic Early Bird
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(35)
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Keyword Pioneer
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(15)
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Renaissance Researcher
(9)
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Unstoppable Β· Gold
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The Questioner
(2)
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Prolific Year Β· Prolific
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Conference Pioneer
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Century Club Β· Silver
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Trend Setter
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(318)
Venues
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Research topics
Machine Learning
(70)
Core Methods
(55)
Optimization & Theory
(35)
Artificial Intelligence
(29)
Core AI
(26)
Learning Types
(18)
Mathematics & Optimization
(14)
Application Areas
(12)
Optimization
(12)
Deep Learning
(11)
Healthcare & Medicine
(6)
Architectures
(6)
Clinical
(6)
Data Science & Analytics
(5)
Bayesian & Probabilistic
(5)
Keywords
causal inference
(10)
rashomon set
(9)
decision tree
(9)
interpretable machine learning
(8)
interpretable model
(8)
treatment effect estimation
(6)
variable importance
(6)
feature selection
(5)
sparse model
(5)
combinatorial optimization
(5)
prototype learning
(4)
dynamic programming
(4)
decision boundary
(3)
model interpretability
(3)
case-based reasoning
(3)
regret bound
(3)
ensemble method
(3)
sparse optimization
(3)
representation learning
(3)
model selection
(3)
Papers
AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation
AAAI 2026
Cosine Similarity is Almost All You Need (for Prototypical-Part Models)
WACV 2026
REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees
UAI 2026
Resolving Predictive Multiplicity for the Rashomon Set
AAAI 2026
Dimension Reduction with Locally Adjusted Graphs
AAAI 2025
How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data
AAAI 2025
Leveraging Predictive Equivalence in Decision Trees
ICML 2025
Models That Are Interpretable But Not Transparent
AISTATS 2025
Near-Optimal Decision Trees in a SPLIT Second
ICML 2025
ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis
NeurIPS 2025
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time
CVPR 2025
This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN
MICCAI 2025
"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts
JMLR 2025
Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference
AAAI 2024
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models
NeurIPS 2024
Improving Decision Sparsity
NeurIPS 2024
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data
AISTATS 2024
Interpretable Generalized Additive Models for Datasets with Missing Values
NeurIPS 2024
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers
NeurIPS 2024
Navigating the Effect of Parametrization for Dimensionality Reduction
NeurIPS 2024
Optimal Sparse Survival Trees
AISTATS 2024
Position: Amazing Things Come From Having Many Good Models
ICML 2024
Safe and Interpretable Estimation of Optimal Treatment Regimes
AISTATS 2024
Sparse and Faithful Explanations Without Sparse Models
AISTATS 2024
Using Noise to Infer Aspects of Simplicity Without Learning
NeurIPS 2024
A Path to Simpler Models Starts With Noise
NeurIPS 2023
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models
NeurIPS 2023
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation
JMLR 2023
OKRidge: Scalable Optimal k-Sparse Ridge Regression
NeurIPS 2023
Optimal Sparse Regression Trees
AAAI 2023
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation
ACL 2023
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance
NeurIPS 2023
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations
NeurIPS 2023
Variable importance matching for causal inference
UAI 2023
Data poisoning attacks on off-policy policy evaluation methods
UAI 2022
Exploring the Whole Rashomon Set of Sparse Decision Trees
NeurIPS 2022
FasterRisk: Fast and Accurate Interpretable Risk Scores
NeurIPS 2022
Fast Sparse Classification for Generalized Linear and Additive Models
AISTATS 2022
Fast Sparse Decision Tree Optimization via Reference Ensembles
AAAI 2022
MALTS: Matching After Learning to Stretch
JMLR 2022
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity
JMLR 2022
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference
JMLR 2021
Multitask Learning for Citation Purpose Classification
NAACL 2021
Regulating Greed Over Time in Multi-Armed Bandits
JMLR 2021
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization
JMLR 2021
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation
UAI 2020
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference
AISTATS 2020
A Transformer Approach to Contextual Sarcasm Detection in Twitter
ACL 2020
Bandits for BMO Functions
ICML 2020
Generalized and Scalable Optimal Sparse Decision Trees
ICML 2020
Metaphor Detection Using Contextual Word Embeddings From Transformers
ACL 2020
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
CVPR 2020
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
JMLR 2019
Interpretable Almost-Exact Matching for Causal Inference
AISTATS 2019
Interpretable Almost Matching Exactly With Instrumental Variables
UAI 2019
Learning Optimized Risk Scores
JMLR 2019
Optimal Sparse Decision Trees
NeurIPS 2019
Reducing Exploration of Dying Arms in Mortal Bandits
UAI 2019
This Looks Like That: Deep Learning for Interpretable Image Recognition
NeurIPS 2019
An Optimization Approach to Learning Falling Rule Lists
AISTATS 2018
Direct Learning to Rank And Rerank
AISTATS 2018
Learning Certifiably Optimal Rule Lists for Categorical Data
JMLR 2018
A Bayesian Framework for Learning Rule Sets for Interpretable Classification
JMLR 2017
Learning Cost-Effective and Interpretable Treatment Regimes
AISTATS 2017
Scalable Bayesian Rule Lists
ICML 2017
CRAFT: ClusteR-specific Assorted Feature selecTion
AISTATS 2016
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes
JMLR 2016
Falling Rule Lists
AISTATS 2015
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
NeurIPS 2014
Learning Theory Analysis for Association Rules and Sequential Event Prediction
JMLR 2013
Machine Learning with Operational Costs
JMLR 2013
The Rate of Convergence of AdaBoost
JMLR 2013
An Integer Optimization Approach to Associative Classification
NeurIPS 2012
Open Problem: Does AdaBoost Always Cycle?
COLT 2012
On Equivalence Relationships Between Classification and Ranking Algorithms
JMLR 2011
Sequential Event Prediction with Association Rules
COLT 2011
The Rate of Convergence of Adaboost
COLT 2011
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost
JMLR 2009
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List
JMLR 2009
Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking
ACL 2008
The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins
JMLR 2004