← Authors

Cynthia Rudin

79 papers · 2004–2026 · 12 conferences · across top CS/AI conferences

Achievements

🐝 Cross-Pollinator (10) 🌈 Renaissance Researcher (7) πŸŒ‰ Interdisciplinary Bridge πŸ—ΊοΈ Taxonomy Completionist (29) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird 🌍 Conference Polyglot (12) πŸƒ Academic Marathon (22) 🌱 Topic Pioneer πŸ† Keyword Champion (2) πŸ”¬ Deep Specialist (13) 🀝 Dynamic Duo (15) πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ—ƒοΈ Keyword Collector (93) ⚑ Prolific Year (12) πŸ’Ž Century Club (77) πŸ”₯ Unstoppable (16) ❓ The Questioner (2)

Conferences

JMLR (19) NIPS (17) AISTATS (13) AAAI (7) ICML (6) UAI (5) ACL (4) COLT (3) CVPR (2) MICCAI (1) NAACL (1) WACV (1)

Top co-authors

Margo Seltzer (15) Alexander Volfovsky (10) Jon Donnelly (9) Chudi Zhong (9) Sudeepa Roy (6) Jiachang Liu (6) Hayden McTavish (5) Chaofan Chen (5) Lesia Semenova (5) Robert E. Schapire (5)

Research topics

Optimization (1)

Keywords

causal inference (10) interpretable machine learning (8) interpretable model (7) rashomon set (7) treatment effect estimation (6) decision tree (6) sparse model (5) feature selection (5) combinatorial optimization (5) variable importance (5) prototype learning (4) model selection (3) generalized additive model (3) model interpretability (3) representation learning (3) case-based reasoning (3) sparse optimization (3) dynamic programming (3) regret bound (3) ensemble method (3)

Papers

Resolving Predictive Multiplicity for the Rashomon Set AAAI 2026
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
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
Leveraging Predictive Equivalence in Decision Trees ICML 2025
Near-Optimal Decision Trees in a SPLIT Second ICML 2025
Models That Are Interpretable But Not Transparent AISTATS 2025
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
Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time CVPR 2025
Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference AAAI 2024
Position: Amazing Things Come From Having Many Good Models ICML 2024
Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data AISTATS 2024
Safe and Interpretable Estimation of Optimal Treatment Regimes AISTATS 2024
Sparse and Faithful Explanations Without Sparse Models AISTATS 2024
Optimal Sparse Survival Trees AISTATS 2024
Interpretable Generalized Additive Models for Datasets with Missing Values NIPS 2024
Navigating the Effect of Parametrization for Dimensionality Reduction NIPS 2024
Improving Decision Sparsity NIPS 2024
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers NIPS 2024
FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models NIPS 2024
Using Noise to Infer Aspects of Simplicity Without Learning NIPS 2024
Optimal Sparse Regression Trees AAAI 2023
A Path to Simpler Models Starts With Noise NIPS 2023
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance NIPS 2023
This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations NIPS 2023
OKRidge: Scalable Optimal k-Sparse Ridge Regression NIPS 2023
Exploring and Interacting with the Set of Good Sparse Generalized Additive Models NIPS 2023
The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation ACL 2023
Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation JMLR 2023
Variable importance matching for causal inference UAI 2023
Data poisoning attacks on off-policy policy evaluation methods UAI 2022
Fast Sparse Decision Tree Optimization via Reference Ensembles AAAI 2022
Fast Sparse Classification for Generalized Linear and Additive Models AISTATS 2022
MALTS: Matching After Learning to Stretch JMLR 2022
Rethinking Nonlinear Instrumental Variable Models through Prediction Validity JMLR 2022
Exploring the Whole Rashomon Set of Sparse Decision Trees NIPS 2022
FasterRisk: Fast and Accurate Interpretable Risk Scores NIPS 2022
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference JMLR 2021
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization JMLR 2021
Multitask Learning for Citation Purpose Classification NAACL 2021
Regulating Greed Over Time in Multi-Armed Bandits JMLR 2021
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference AISTATS 2020
A Transformer Approach to Contextual Sarcasm Detection in Twitter ACL 2020
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models CVPR 2020
Metaphor Detection Using Contextual Word Embeddings From Transformers ACL 2020
Generalized and Scalable Optimal Sparse Decision Trees ICML 2020
Bandits for BMO Functions ICML 2020
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation UAI 2020
Learning Optimized Risk Scores JMLR 2019
All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously JMLR 2019
This Looks Like That: Deep Learning for Interpretable Image Recognition NIPS 2019
Optimal Sparse Decision Trees NIPS 2019
Interpretable Almost Matching Exactly With Instrumental Variables UAI 2019
Interpretable Almost-Exact Matching for Causal Inference AISTATS 2019
Reducing Exploration of Dying Arms in Mortal Bandits UAI 2019
An Optimization Approach to Learning Falling Rule Lists AISTATS 2018
Learning Certifiably Optimal Rule Lists for Categorical Data JMLR 2018
Direct Learning to Rank And Rerank AISTATS 2018
Learning Cost-Effective and Interpretable Treatment Regimes AISTATS 2017
A Bayesian Framework for Learning Rule Sets for Interpretable Classification JMLR 2017
Scalable Bayesian Rule Lists ICML 2017
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes JMLR 2016
CRAFT: ClusteR-specific Assorted Feature selecTion AISTATS 2016
Falling Rule Lists AISTATS 2015
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification NIPS 2014
Machine Learning with Operational Costs JMLR 2013
Learning Theory Analysis for Association Rules and Sequential Event Prediction JMLR 2013
The Rate of Convergence of AdaBoost JMLR 2013
An Integer Optimization Approach to Associative Classification NIPS 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
The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List JMLR 2009
Margin-based Ranking and an Equivalence between AdaBoost and RankBoost 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