Cynthia Rudin - RankMe

289 papers A* 46A 19B 1C 4Misc 4Journal 186Unranked 29

Year	Rank	Type	Title / Venue / Authors
2026	A*	`conf`	AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation. AAAI Stephen Ni-Hahn, Rico Zhu, Jerry Yin, Yue Jiang, Cynthia Rudin, Simon Mak
2026	J	`jnl`	REALITrees: Rashomon Ensemble Active Learning for Interpretable Trees. CoRR Simon D. Nguyen, Hayden McTavish, Kentaro Hoffman, Cynthia Rudin, Tyler H. McCormick
2026	A*	`conf`	Resolving Predictive Multiplicity for the Rashomon Set. AAAI Parian Haghighat, Hadis Anahideh, Cynthia Rudin
2026	J	`jnl`	Resolving Predictive Multiplicity for the Rashomon Set. CoRR Parian Haghighat, Hadis Anahideh, Cynthia Rudin
2025	J	`jnl`	"What is Different Between These Datasets?" A Framework for Explaining Data Distribution Shifts. J. Mach. Learn. Res. Varun Babbar, Zhicheng Guo, Cynthia Rudin
2025	J	`jnl`	AutoSchA: Automatic Hierarchical Music Representations via Multi-Relational Node Isolation. CoRR Stephen Ni-Hahn, Rico Zhu, Jerry Yin, Yue Jiang, Cynthia Rudin, Simon Mak
2025	J	`jnl`	Data Fusion for Partial Identification of Causal Effects. CoRR Quinn Lanners, Cynthia Rudin, Alexander Volfovsky, Harsh Parikh
2025	A*	`conf`	Dimension Reduction with Locally Adjusted Graphs. AAAI Yingfan Wang, Yiyang Sun, Haiyang Huang, Cynthia Rudin
2025	J	`jnl`	Doctor Rashomon and the UNIVERSE of Madness: Variable Importance with Unobserved Confounding and the Rashomon Effect. CoRR Jon Donnelly, Srikar Katta, Emanuele Borgonovo, Cynthia Rudin
2025	J	`jnl`	Fast and interpretable mortality risk scores for critical care patients. J. Am. Medical Informatics Assoc. Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin
2025	A*	`conf`	How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data. AAAI Ishaan Maitra, Raymond Lin, Eric Chen, Jon Donnelly, Sanja Scepanovic, Cynthia Rudin
2025	J	`jnl`	How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data. CoRR Ishaan Maitra, Raymond Lin, Eric Chen, Jon Donnelly, Sanja Scepanovic, Cynthia Rudin
2025	J	`jnl`	It's LIT! Reliability-Optimized LLMs with Inspectable Tools. CoRR Ruixin Zhang, Jon Donnelly, Zhicheng Guo, Ghazal Khalighinejad, Haiyang Huang, Alina Jade Barnett, Cynthia Rudin
2025	J	`jnl`	Leo Breiman, the Rashomon Effect, and the Occam Dilemma. CoRR Cynthia Rudin
2025	A*	`conf`	Leveraging Predictive Equivalence in Decision Trees. ICML Hayden McTavish, Zachery Boner, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin
2025	J	`jnl`	Leveraging Predictive Equivalence in Decision Trees. CoRR Hayden McTavish, Zachery Boner, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin
2025	A	`conf`	Models That Are Interpretable But Not Transparent. AISTATS Chudi Zhong, Panyu Chen, Cynthia Rudin
2025	J	`jnl`	Models That Are Interpretable But Not Transparent. CoRR Chudi Zhong, Panyu Chen, Cynthia Rudin
2025	J	`jnl`	Near Optimal Decision Trees in a SPLIT Second. CoRR Varun Babbar, Hayden McTavish, Cynthia Rudin, Margo I. Seltzer
2025	A*	`conf`	Near-Optimal Decision Trees in a SPLIT Second. ICML Varun Babbar, Hayden McTavish, Cynthia Rudin, Margo I. Seltzer
2025	J	`jnl`	NodMAISI: Nodule-Oriented Medical AI for Synthetic Imaging. CoRR Fakrul Islam Tushar, Ehsan Samei, Cynthia Rudin, Joseph Y. Lo
2025	J	`jnl`	ProGress: Structured Music Generation via Graph Diffusion and Hierarchical Music Analysis. CoRR Stephen Ni-Hahn, Chao Péter Yang, Mingchen Ma, Cynthia Rudin, Simon Mak, Yue Jiang
2025	A*	`conf`	Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time. CVPR Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, Hayden McTavish, Chaofan Chen, Cynthia Rudin
2025	J	`jnl`	Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time. CoRR Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, Hayden McTavish, Chaofan Chen, Cynthia Rudin
2025	J	`jnl`	Revisiting Broken Windows Theory. CoRR Ziyao Cui, Erick Jiang, Nicholas Sortisio, Haiyan Wang, Eric Chen, Cynthia Rudin
2025	—	`conf`	This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN. MICCAI (14) Dennis Tang, Jon Donnelly, Alina Jade Barnett, Lesia Semenova, Jin Jing, Peter Hadar, Ioannis Karakis, Olga Selioutski, Kehan Zhao, M. Brandon Westover, Cynthia Rudin
2025	J	`jnl`	This EEG Looks Like These EEGs: Interpretable Interictal Epileptiform Discharge Detection With ProtoEEG-kNN. CoRR Dennis Tang, Jon Donnelly, Alina Jade Barnett, Lesia Semenova, Jin Jing, Peter Hadar, Ioannis Karakis, Olga Selioutski, Kehan Zhao, M. Brandon Westover, Cynthia Rudin
2025	J	`jnl`	What You See is (Usually) What You Get: Multimodal Prototype Networks that Abstain from Expensive Modalities. CoRR Muchang Bahng, Charlie Berens, Jon Donnelly, Eric Chen, Chaofan Chen, Cynthia Rudin
2025	J	`jnl`	dame-flame: A J. Stat. Softw. Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2024	—	`conf`	A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis. ISMIR Stephen Ni-Hahn, Weihan Xu, Zirui Yin, Rico Zhu, Simon Mak, Yue Jiang, Cynthia Rudin
2024	J	`jnl`	A New Dataset, Notation Software, and Representation for Computational Schenkerian Analysis. CoRR Stephen Ni-Hahn, Weihan Xu, Jerry Yin, Rico Zhu, Simon Mak, Yue Jiang, Cynthia Rudin
2024	J	`jnl`	Amazing Things Come From Having Many Good Models. CoRR Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
2024	J	`jnl`	Data Poisoning Attacks on Off-Policy Policy Evaluation Methods. CoRR Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
2024	J	`jnl`	Dimension Reduction with Locally Adjusted Graphs. CoRR Yingfan Wang, Yiyang Sun, Haiyang Huang, Cynthia Rudin
2024	A*	`conf`	Evaluating Pre-trial Programs Using Interpretable Machine Learning Matching Algorithms for Causal Inference. AAAI Travis Seale-Carlisle, Saksham Jain, Courtney Lee, Caroline Levenson, Swathi Ramprasad, Brandon Garrett, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2024	—	`conf`	FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. CVPR Workshops Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin
2024	J	`jnl`	FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography. CoRR Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin
2024	A*	`conf`	FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models. NeurIPS Jiachang Liu, Rui Zhang, Cynthia Rudin
2024	J	`jnl`	FastSurvival: Hidden Computational Blessings in Training Cox Proportional Hazards Models. CoRR Jiachang Liu, Rui Zhang, Cynthia Rudin
2024	A*	`conf`	Improving Decision Sparsity. NeurIPS Yiyang Sun, Tong Wang, Cynthia Rudin
2024	J	`jnl`	Improving Decision Sparsity. CoRR Yiyang Sun, Tong Wang, Cynthia Rudin
2024	J	`jnl`	Integrated Single-cell Multiomic Analysis of HIV Latency Reversal Reveals Novel Regulators of Viral Reactivation. Genom. Proteom. Bioinform. Manickam Ashokkumar, Wenwen Mei, Jackson J. Peterson, Yuriko Harigaya, David M. Murdoch, David M. Margolis, Caleb Kornfein, Alexander X. Oesterling, Zhicheng Guo, Cynthia Rudin, Yuchao Jiang, Edward P. Browne
2024	A	`conf`	Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. AISTATS Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
2024	A*	`conf`	Interpretable Generalized Additive Models for Datasets with Missing Values. NeurIPS Hayden McTavish, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin
2024	J	`jnl`	Interpretable Generalized Additive Models for Datasets with Missing Values. CoRR Hayden McTavish, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin
2024	A*	`conf`	Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. NeurIPS Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen
2024	J	`jnl`	Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. CoRR Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen
2024	J	`jnl`	Learning From Alarms: A Robust Learning Approach for Accurate Photoplethysmography-Based Atrial Fibrillation Detection Using Eight Million Samples Labeled With Imprecise Arrhythmia Alarms. IEEE J. Biomed. Health Informatics Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Amit J. Shah, Duc H. Do, Randall J. Lee, Gari D. Clifford, Fadi B. Nahab, Xiao Hu
2024	A*	`conf`	Navigating the Effect of Parametrization for Dimensionality Reduction. NeurIPS Haiyang Huang, Yingfan Wang, Cynthia Rudin
2024	J	`jnl`	Navigating the Effect of Parametrization for Dimensionality Reduction. CoRR Haiyang Huang, Yingfan Wang, Cynthia Rudin
2024	A	`conf`	Optimal Sparse Survival Trees. AISTATS Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin
2024	J	`jnl`	Optimal Sparse Survival Trees. CoRR Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin
2024	J	`jnl`	Phononic materials with effectively scale-separated hierarchical features using interpretable machine learning. CoRR Mary Bastawrous, Zhi Chen, Alexander C. Ogren, Chiara Daraio, Cynthia Rudin, L. Catherine Brinson
2024	A*	`conf`	Position: Amazing Things Come From Having Many Good Models. ICML Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner
2024	A	`conf`	Safe and Interpretable Estimation of Optimal Treatment Regimes. AISTATS Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky
2024	A*	`conf`	SentHYMNent: An Interpretable and Sentiment-Driven Model for Algorithmic Melody Harmonization. KDD Stephen Hahn, Jerry Yin, Rico Zhu, Weihan Xu, Yue Jiang, Simon Mak, Cynthia Rudin
2024	J	`jnl`	SiamQuality: A ConvNet-Based Foundation Model for Imperfect Physiological Signals. CoRR Cheng Ding, Zhicheng Guo, Zhaoliang Chen, Randall J. Lee, Cynthia Rudin, Xiao Hu
2024	A	`conf`	Sparse and Faithful Explanations Without Sparse Models. AISTATS Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin
2024	J	`jnl`	Sparse and Faithful Explanations Without Sparse Models. CoRR Yiyang Sun, Zhi Chen, Vittorio Orlandi, Tong Wang, Cynthia Rudin
2024	J	`jnl`	Sparse learned kernels for interpretable and efficient medical time series processing. Nat. Mac. Intell. Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin
2024	J	`jnl`	This Looks Better than That: Better Interpretable Models with ProtoPNeXt. CoRR Frank Willard, Luke Moffett, Emmanuel Mokel, Jon Donnelly, Stark Guo, Julia Yang, Giyoung Kim, Alina Jade Barnett, Cynthia Rudin
2024	A*	`conf`	Using Noise to Infer Aspects of Simplicity Without Learning. NeurIPS Zachery Boner, Harry Chen, Lesia Semenova, Ronald Parr, Cynthia Rudin
2024	J	`jnl`	What is different between these datasets? CoRR Varun Babbar, Zhicheng Guo, Cynthia Rudin
2023	J	`jnl`	A Double Machine Learning Approach to Combining Experimental and Observational Data. CoRR Marco Morucci, Vittorio Orlandi, Harsh Parikh, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2023	A*	`conf`	A Path to Simpler Models Starts With Noise. NeurIPS Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin
2023	J	`jnl`	A Path to Simpler Models Starts With Noise. CoRR Lesia Semenova, Harry Chen, Ronald Parr, Cynthia Rudin
2023	J	`jnl`	A Self-Supervised Algorithm for Denoising Photoplethysmography Signals for Heart Rate Estimation from Wearables. CoRR Pranay Jain, Cheng Ding, Cynthia Rudin, Xiao Hu
2023	—	`conf`	A user interface to communicate interpretable AI decisions to radiologists (Erratum). Image Perception, Observer Performance, and Technology Assessment Yanchen Ou, Alina Jade Barnett, Anika Mitra, Fides Regina Schwartz, Chaofan Chen, Lars J. Grimm, Joseph Y. Lo, Cynthia Rudin
2023	—	`conf`	A user interface to communicate interpretable AI decisions to radiologists. Image Perception, Observer Performance, and Technology Assessment Yanchen Jessie Ou, Alina Jade Barnett, Anika Mitra, Fides Regina Schwartz, Chaofan Chen, Lars J. Grimm, Joseph Y. Lo, Cynthia Rudin
2023	A*	`conf`	An Interpretable, Flexible, and Interactive Probabilistic Framework for Melody Generation. KDD Stephen Hahn, Rico Zhu, Simon Mak, Cynthia Rudin, Yue Jiang
2023	J	`jnl`	Estimating Trustworthy and Safe Optimal Treatment Regimes. CoRR Harsh Parikh, Quinn Lanners, Zade Akras, Sahar F. Zafar, M. Brandon Westover, Cynthia Rudin, Alexander Volfovsky
2023	A*	`conf`	Exploring and Interacting with the Set of Good Sparse Generalized Additive Models. NeurIPS Chudi Zhong, Zhi Chen, Jiachang Liu, Margo I. Seltzer, Cynthia Rudin
2023	J	`jnl`	Fast and Interpretable Mortality Risk Scores for Critical Care Patients. CoRR Chloe Qinyu Zhu, Muhang Tian, Lesia Semenova, Jiachang Liu, Jack Xu, Joseph Scarpa, Cynthia Rudin
2023	J	`jnl`	From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues. AIS Trans. Hum. Comput. Interact. Lina Zhou, Cynthia Rudin, Matthew C. Gombolay, Jim Spohrer, Michelle Zhou, Souren Paul
2023	J	`jnl`	From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. CoRR Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page
2023	J	`jnl`	Globally-Consistent Rule-Based Summary-Explanations for Machine Learning Models: Application to Credit-Risk Evaluation. J. Mach. Learn. Res. Cynthia Rudin, Yaron Shaposhnik
2023	J	`jnl`	Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data. CoRR Srikar Katta, Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
2023	J	`jnl`	Learned Kernels for Interpretable and Efficient PPG Signal Quality Assessment and Artifact Segmentation. CoRR Sully F. Chen, Zhicheng Guo, Cheng Ding, Xiao Hu, Cynthia Rudin
2023	—	`conf`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CHIL Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	J	`jnl`	Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana
2023	J	`jnl`	OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems. CoRR Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin
2023	A*	`conf`	OKRidge: Scalable Optimal k-Sparse Ridge Regression. NeurIPS Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin
2023	A*	`conf`	Optimal Sparse Regression Trees. AAAI Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin
2023	J	`jnl`	ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges. CoRR Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, M. Brandon Westover
2023	J	`jnl`	Reconsideration on evaluation of machine learning models in continuous monitoring using wearables. CoRR Cheng Ding, Zhicheng Guo, Cynthia Rudin, Ran Xiao, Fadi B. Nahab, Xiao Hu
2023	J	`jnl`	SiamAF: Learning Shared Information from ECG and PPG Signals for Robust Atrial Fibrillation Detection. CoRR Zhicheng Guo, Cheng Ding, Duc H. Do, Amit J. Shah, Randall J. Lee, Xiao Hu, Cynthia Rudin
2023	—	`conf`	The Mechanical Bard: An Interpretable Machine Learning Approach to Shakespearean Sonnet Generation. ACL (2) Edwin Agnew, Michelle Qiu, Lily Zhu, Sam Wiseman, Cynthia Rudin
2023	A*	`conf`	The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. NeurIPS Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne
2023	J	`jnl`	The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. CoRR Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne
2023	A*	`conf`	This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. NeurIPS Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin
2023	J	`jnl`	This Looks Like Those: Illuminating Prototypical Concepts Using Multiple Visualizations. CoRR Chiyu Ma, Brandon Zhao, Chaofan Chen, Cynthia Rudin
2023	J	`jnl`	Uncertainty Quantification of Bandgaps in Acoustic Metamaterials with Stochastic Geometric Defects and Material Properties. CoRR Han Zhang, Rayehe Karimi Mahabadi, Cynthia Rudin, Johann Guilleminot, L. Catherine Brinson
2023	J	`jnl`	Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models. CoRR Zhi Chen, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin
2023	A	`conf`	Variable importance matching for causal inference. UAI Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page
2022	J	`jnl`	A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decis. Support Syst. Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang
2022	J	`jnl`	Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. INFORMS J. Comput. Tong Wang, Cynthia Rudin
2022	A	`conf`	Data poisoning attacks on off-policy policy evaluation methods. UAI Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
2022	A*	`conf`	Exploring the Whole Rashomon Set of Sparse Decision Trees. NeurIPS Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin
2022	J	`jnl`	Exploring the Whole Rashomon Set of Sparse Decision Trees. CoRR Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin
2022	J	`jnl`	Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. CoRR Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo I. Seltzer
2022	A	`conf`	Fast Sparse Classification for Generalized Linear and Additive Models. AISTATS Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin
2022	J	`jnl`	Fast Sparse Classification for Generalized Linear and Additive Models. CoRR Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin
2022	A*	`conf`	Fast Sparse Decision Tree Optimization via Reference Ensembles. AAAI Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer
2022	—	`conf`	Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. CIKM Workshops Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Mango I. Seltzer
2022	A*	`conf`	FasterRisk: Fast and Accurate Interpretable Risk Scores. NeurIPS Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin
2022	J	`jnl`	FasterRisk: Fast and Accurate Interpretable Risk Scores. CoRR Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin
2022	—	`conf`	Interpretable deep learning models for better clinician-AI communication in clinical mammography. Image Perception, Observer Performance, and Technology Assessment Alina Jade Barnett, Vaibhav Sharma, Neel Gajjar, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin
2022	J	`jnl`	MALTS: Matching After Learning to Stretch. J. Mach. Learn. Res. Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
2022	J	`jnl`	Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. CoRR Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover
2022	—	`conf`	On the Existence of Simpler Machine Learning Models. FAccT Lesia Semenova, Cynthia Rudin, Ronald Parr
2022	J	`jnl`	Optimal Sparse Regression Trees. CoRR Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin
2022	J	`jnl`	Rethinking Nonlinear Instrumental Variable Models through Prediction Validity. J. Mach. Learn. Res. Chunxiao Li, Cynthia Rudin, Tyler H. McCormick
2022	J	`jnl`	SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation. CoRR Haiyang Huang, Zhi Chen, Cynthia Rudin
2022	J	`jnl`	There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. CoRR Yishay Mansour, Michal Moshkovitz, Cynthia Rudin
2022	—	`conf`	TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. IEEE VIS (Short Papers) Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer
2022	J	`jnl`	TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. CoRR Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer
2022	J	`jnl`	Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It. CoRR Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar F. Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover
2021	J	`jnl`	A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations. CoRR Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang
2021	J	`jnl`	A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Manag. Sci. Beau Coker, Cynthia Rudin, Gary King
2021	J	`jnl`	A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat. Mach. Intell. Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin
2021	J	`jnl`	BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales. CoRR Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin
2021	C	`conf`	Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. AIES Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan
2021	J	`jnl`	Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. CoRR Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan
2021	J	`jnl`	FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. J. Mach. Learn. Res. Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2021	J	`jnl`	Fast Sparse Decision Tree Optimization via Reference Ensembles. CoRR Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer
2021	J	`jnl`	How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. CoRR Zhi Chen, Alexander C. Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin
2021	J	`jnl`	IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. CoRR Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin
2021	J	`jnl`	Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. CoRR Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong
2021	J	`jnl`	Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. CoRR Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin
2021	J	`jnl`	Multitask Learning for Citation Purpose Classification. CoRR Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin
2021	J	`jnl`	Playing Codenames with Language Graphs and Word Embeddings. CoRR Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin
2021	J	`jnl`	Playing Codenames with Language Graphs and Word Embeddings. J. Artif. Intell. Res. Divya Koyyalagunta, Anna Y. Sun, Rachel Lea Draelos, Cynthia Rudin
2021	J	`jnl`	Regulating Greed Over Time in Multi-Armed Bandits. J. Mach. Learn. Res. Stefano Tracà, Cynthia Rudin, Weiyu Yan
2021	J	`jnl`	There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. CoRR Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin
2021	J	`jnl`	There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. Trans. Assoc. Comput. Linguistics Jianyou Wang, Cynthia Rudin, Yuren Zhou, Christopher Suh, Xiaoxuan Zhang
2021	J	`jnl`	Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization. J. Mach. Learn. Res. Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik
2021	J	`jnl`	dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference. CoRR Neha R. Gupta, Vittorio Orlandi, Chia-Rui Chang, Tianyu Wang, Marco Morucci, Pritam Dey, Thomas J. Howell, Xian Sun, Angikar Ghosal, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	—	`conf`	A Transformer Approach to Contextual Sarcasm Detection in Twitter. Fig-Lang@ACL Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Lea Draelos, Cynthia Rudin
2020	J	`jnl`	AI reflections in 2019. Nat. Mach. Intell. Alexander S. Rich, Cynthia Rudin, David M. P. Jacoby, Robin Freeman, Oliver R. Wearn, Henry Shevlin, Kanta Dihal, Seán S. ÓhÉigeartaigh, James Butcher, Marco Lippi, Przemyslaw Palka, Paolo Torroni, Shannon Wongvibulsin, Edmon Begoli, Gisbert Schneider, Stephen Cave, Mona Sloane, Emanuel Moss, Iyad Rahwan, Ken Goldberg, David Howard, Luciano Floridi, Jack Stilgoe
2020	A	`conf`	Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. UAI Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	J	`jnl`	Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. CoRR Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	A	`conf`	Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. AISTATS M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	J	`jnl`	Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. CoRR M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2020	A*	`conf`	Bandits for BMO Functions. ICML Tianyu Wang, Cynthia Rudin
2020	J	`jnl`	Bandits for BMO Functions. CoRR Tianyu Wang, Cynthia Rudin
2020	J	`jnl`	Concept Whitening for Interpretable Image Recognition. CoRR Zhi Chen, Yijie Bei, Cynthia Rudin
2020	J	`jnl`	Concept whitening for interpretable image recognition. Nat. Mach. Intell. Zhi Chen, Yijie Bei, Cynthia Rudin
2020	J	`jnl`	Cryo-ZSSR: multiple-image super-resolution based on deep internal learning. CoRR Qinwen Huang, Ye Zhou, Xiaochen Du, Reed Chen, Jianyou Wang, Cynthia Rudin, Alberto Bartesaghi
2020	J	`jnl`	Exploring the cloud of variable importance for the set of all good models. Nat. Mach. Intell. Jiayun Dong, Cynthia Rudin
2020	J	`jnl`	Generalized Optimal Sparse Decision Trees. CoRR Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer
2020	A*	`conf`	Generalized and Scalable Optimal Sparse Decision Trees. ICML Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer
2020	J	`jnl`	In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction. CoRR Caroline Wang, Bin Han, Bhrij Patel, Feroze Mohideen, Cynthia Rudin
2020	—	`conf`	Metaphor Detection Using Contextual Word Embeddings From Transformers. Fig-Lang@ACL Jerry W. Liu, Nathan O'Hara, Alexander Rubin, Rachel Lea Draelos, Cynthia Rudin
2020	A*	`conf`	PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CVPR Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin
2020	J	`jnl`	PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CoRR Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin
2020	—	`conf`	Towards Practical Lipschitz Bandits. FODS Tianyu Wang, Weicheng Ye, Dawei Geng, Cynthia Rudin
2020	J	`jnl`	Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization. CoRR Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik
2019	J	`jnl`	A Practical Bandit Method with Advantages in Neural Network Tuning. CoRR Tianyu Wang, Dawei Geng, Cynthia Rudin
2019	J	`jnl`	A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning. CoRR Lesia Semenova, Cynthia Rudin
2019	J	`jnl`	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. Aaron Fisher, Cynthia Rudin, Francesca Dominici
2019	A*	`conf`	Do Simpler Models Exist and How Can We Find Them? KDD Cynthia Rudin
2019	A	`conf`	Interpretable Almost Matching Exactly With Instrumental Variables. UAI M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2019	A	`conf`	Interpretable Almost-Exact Matching for Causal Inference. AISTATS Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2019	J	`jnl`	Interpretable Almost-Matching-Exactly With Instrumental Variables. CoRR M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2019	B	`conf`	Interpretable Image Recognition with Hierarchical Prototypes. HCOMP Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
2019	J	`jnl`	Interpretable Image Recognition with Hierarchical Prototypes. CoRR Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin
2019	J	`jnl`	Learning Optimized Risk Scores. J. Mach. Learn. Res. Berk Ustun, Cynthia Rudin
2019	A*	`conf`	Optimal Sparse Decision Trees. NeurIPS Xiyang Hu, Cynthia Rudin, Margo I. Seltzer
2019	J	`jnl`	Optimal Sparse Decision Trees. CoRR Xiyang Hu, Cynthia Rudin, Margo I. Seltzer
2019	A	`conf`	Reducing Exploration of Dying Arms in Mortal Bandits. UAI Stefano Tracà, Weiyu Yan, Cynthia Rudin
2019	J	`jnl`	Reducing Exploration of Dying Arms in Mortal Bandits. CoRR Stefano Tracà, Cynthia Rudin, Weiyu Yan
2019	J	`jnl`	Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. Cynthia Rudin
2019	J	`jnl`	The Big Data Newsvendor: Practical Insights from Machine Learning. Oper. Res. Gah-Yi Ban, Cynthia Rudin
2019	J	`jnl`	The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis. CoRR Cynthia Rudin, David Carlson
2019	A*	`conf`	This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su
2019	J	`jnl`	Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models. CoRR Jiayun Dong, Cynthia Rudin
2018	J	`jnl`	A Minimax Surrogate Loss Approach to Conditional Difference Estimation. CoRR Siong Thye Goh, Cynthia Rudin
2018	J	`jnl`	A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. CoRR Beau Coker, Cynthia Rudin, Gary King
2018	J	`jnl`	An Interpretable Model with Globally Consistent Explanations for Credit Risk. CoRR Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang
2018	A	`conf`	An Optimization Approach to Learning Falling Rule Lists. AISTATS Chaofan Chen, Cynthia Rudin
2018	J	`jnl`	Bayesian Patchworks: An Approach to Case-Based Reasoning. CoRR Ramin Moghaddass, Cynthia Rudin
2018	J	`jnl`	Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference. CoRR Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky
2018	A*	`conf`	Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions. AAAI Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin
2018	A	`conf`	Direct Learning to Rank And Rerank. AISTATS Cynthia Rudin, Yining Wang
2018	J	`jnl`	Direct Learning to Rank and Rerank. CoRR Cynthia Rudin, Yining Wang
2018	J	`jnl`	Learning customized and optimized lists of rules with mathematical programming. Math. Program. Comput. Cynthia Rudin, Seyda Ertekin
2018	J	`jnl`	MALTS: Matching After Learning to Stretch. CoRR Harsh Parikh, Cynthia Rudin, Alexander Volfovsky
2018	—	`conf`	New Techniques for Preserving Global Structure and Denoising With Low Information Loss in Single-Image Super-Resolution. CVPR Workshops Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin
2018	J	`jnl`	New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution. CoRR Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin
2018	J	`jnl`	Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. Interfaces Cynthia Rudin, Berk Ustun
2018	J	`jnl`	Please Stop Explaining Black Box Models for High Stakes Decisions. CoRR Cynthia Rudin
2018	J	`jnl`	Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. CoRR John Benhart, Tianlin Duan, Peter Hase, Liuyi Zhu, Cynthia Rudin
2018	J	`jnl`	The age of secrecy and unfairness in recidivism prediction. CoRR Cynthia Rudin, Caroline Wang, Beau Coker
2018	J	`jnl`	This looks like that: deep learning for interpretable image recognition. CoRR Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, Cynthia Rudin
2017	J	`jnl`	A Bayesian Framework for Learning Rule Sets for Interpretable Classification. J. Mach. Learn. Res. Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille
2017	J	`jnl`	An optimization approach to learning falling rule lists. CoRR Chaofan Chen, Cynthia Rudin
2017	J	`jnl`	Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect. CoRR Tong Wang, Cynthia Rudin
2017	J	`jnl`	Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions. CoRR Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin
2017	J	`jnl`	Dimension Reduction for Robust Covariate Shift Correction. CoRR Fulton Wang, Cynthia Rudin
2017	J	`jnl`	FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. CoRR Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, Tianyu Wang
2017	J	`jnl`	Learning Certifiably Optimal Rule Lists for Categorical Data. CoRR Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin
2017	J	`jnl`	Learning Certifiably Optimal Rule Lists for Categorical Data. J. Mach. Learn. Res. Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin
2017	A*	`conf`	Learning Certifiably Optimal Rule Lists. KDD Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin
2017	A	`conf`	Learning Cost-Effective and Interpretable Treatment Regimes. AISTATS Himabindu Lakkaraju, Cynthia Rudin
2017	A*	`conf`	Optimized Risk Scores. KDD Berk Ustun, Cynthia Rudin
2017	A*	`conf`	Scalable Bayesian Rule Lists. ICML Hongyu Yang, Cynthia Rudin, Margo I. Seltzer
2016	A*	`conf`	Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. KDD Benjamin Letham, Lydia M. Letham, Cynthia Rudin
2016	A*	`conf`	Bayesian Rule Sets for Interpretable Classification. ICDM Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille
2016	A	`conf`	CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola
2016	J	`jnl`	Interpretable Machine Learning Models for the Digital Clock Drawing Test. CoRR William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, Dana L. Penney
2016	J	`jnl`	Learning Cost-Effective Treatment Regimes using Markov Decision Processes. CoRR Himabindu Lakkaraju, Cynthia Rudin
2016	J	`jnl`	Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test. Mach. Learn. William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, David J. Libon, Rodney Swenson, Catherine C. Price, Melissa Lamar, Dana L. Penney
2016	J	`jnl`	Scalable Bayesian Rule Lists. CoRR Hongyu Yang, Cynthia Rudin, Margo I. Seltzer
2016	J	`jnl`	Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. Berk Ustun, Cynthia Rudin
2016	J	`jnl`	The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes. J. Mach. Learn. Res. Ramin Moghaddass, Cynthia Rudin, David Madigan
2015	J	`jnl`	A Bayesian Approach to Learning Scoring Systems. Big Data Seyda Ertekin, Cynthia Rudin
2015	J	`jnl`	CRAFT: ClusteR-specific Assorted Feature selecTion. CoRR Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola
2015	J	`jnl`	Causal Falling Rule Lists. CoRR Fulton Wang, Cynthia Rudin
2015	A	`conf`	Falling Rule Lists. AISTATS Fulton Wang, Cynthia Rudin
2015	J	`jnl`	Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. Big Data Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri
2015	J	`jnl`	Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge. Mach. Learn. Theja Tulabandhula, Cynthia Rudin
2015	J	`jnl`	Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model. CoRR Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
2015	J	`jnl`	Learning Optimized Or's of And's. CoRR Tong Wang, Cynthia Rudin
2015	J	`jnl`	Or's of And's for Interpretable Classification, with Application to Context-Aware Recommender Systems. CoRR Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille
2015	J	`jnl`	Regulating Greed Over Time. CoRR Stefano Tracà, Cynthia Rudin
2015	J	`jnl`	Supersparse Linear Integer Models for Optimized Medical Scoring Systems. CoRR Berk Ustun, Cynthia Rudin
2015	J	`jnl`	The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. CoRR Been Kim, Cynthia Rudin, Julie A. Shah
2014	A	`conf`	A Statistical Learning Theory Framework for Supervised Pattern Discovery. SDM Jonathan H. Huggins, Cynthia Rudin
2014	A*	`conf`	Algorithms for interpretable machine learning. KDD Cynthia Rudin
2014	J	`jnl`	Analytics for Power Grid Distribution Reliability in New York City. Interfaces Cynthia Rudin, Seyda Ertekin, Rebecca J. Passonneau, Axinia Radeva, Ashish Tomar, Boyi Xie, Stanley Lewis, Mark Riddle, Debbie Pangsrivinij, Tyler H. McCormick
2014	J	`jnl`	Approximating the crowd. Data Min. Knowl. Discov. Seyda Ertekin, Cynthia Rudin, Haym Hirsh
2014	J	`jnl`	Box Drawings for Learning with Imbalanced Data. CoRR Siong Thye Goh, Cynthia Rudin
2014	A*	`conf`	Box drawings for learning with imbalanced data. KDD Siong Thye Goh, Cynthia Rudin
2014	J	`jnl`	Falling Rule Lists. CoRR Fulton Wang, Cynthia Rudin
2014	Misc	`conf`	Generalization Bounds for Learning with Linear and Quadratic Side Knowledge. ISAIM Theja Tulabandhula, Cynthia Rudin
2014	J	`jnl`	Generalization Bounds for Learning with Linear, Polygonal, Quadratic and Conic Side Knowledge. CoRR Theja Tulabandhula, Cynthia Rudin
2014	J	`jnl`	Learning about meetings. Data Min. Knowl. Discov. Been Kim, Cynthia Rudin
2014	J	`jnl`	Machine learning for science and society. Mach. Learn. Cynthia Rudin, Kiri L. Wagstaff
2014	J	`jnl`	Methods and Models for Interpretable Linear Classification. CoRR Berk Ustun, Cynthia Rudin
2014	—	`conf`	Modeling Weather Impact on a Secondary Electrical Grid. ANT/SEIT Dingquan Wang, Rebecca J. Passonneau, Michael Collins, Cynthia Rudin
2014	J	`jnl`	On combining machine learning with decision making. Mach. Learn. Theja Tulabandhula, Cynthia Rudin
2014	J	`jnl`	Preference Learning (Dagstuhl Seminar 14101). Dagstuhl Reports Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, Scott Sanner
2014	Misc	`conf`	Robust Optimization using Machine Learning for Uncertainty Sets. ISAIM Theja Tulabandhula, Cynthia Rudin
2014	J	`jnl`	Robust Optimization using Machine Learning for Uncertainty Sets. CoRR Theja Tulabandhula, Cynthia Rudin
2014	—	`conf`	The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification. NIPS Been Kim, Cynthia Rudin, Julie A. Shah
2014	J	`jnl`	Tire Changes, Fresh Air, and Yellow Flags: Challenges in Predictive Analytics for Professional Racing. Big Data Theja Tulabandhula, Cynthia Rudin
2014	Misc	`conf`	Toward a Theory of Pattern Discovery. ISAIM Jonathan H. Huggins, Cynthia Rudin
2013	—	`conf`	An Interpretable Stroke Prediction Model using Rules and Bayesian Analysis. AAAI (Late-Breaking Developments) Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan
2013	—	`conf`	Detecting Patterns of Crime with Series Finder. AAAI (Late-Breaking Developments) Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri
2013	J	`jnl`	Growing a list. Data Min. Knowl. Discov. Benjamin Letham, Cynthia Rudin, Katherine A. Heller
2013	J	`jnl`	Learning About Meetings. CoRR Been Kim, Cynthia Rudin
2013	J	`jnl`	Learning theory analysis for association rules and sequential event prediction. J. Mach. Learn. Res. Cynthia Rudin, Benjamin Letham, David Madigan
2013	—	`conf`	Learning to Detect Patterns of Crime. ECML/PKDD (3) Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri
2013	—	`conf`	Machine Learning for Meeting Analysis. AAAI (Late-Breaking Developments) Been Kim, Cynthia Rudin
2013	J	`jnl`	Machine learning with operational costs. J. Mach. Learn. Res. Theja Tulabandhula, Cynthia Rudin
2013	—	`conf`	Predicting Power Failures with Reactive Point Processes. AAAI (Late-Breaking Developments) Seyda Ertekin, Cynthia Rudin, Tyler H. McCormick
2013	J	`jnl`	Sequential event prediction. Mach. Learn. Benjamin Letham, Cynthia Rudin, David Madigan
2013	—	`conf`	Supersparse Linear Integer Models for Predictive Scoring Systems. AAAI (Late-Breaking Developments) Berk Ustun, Stefano Tracà, Cynthia Rudin
2013	J	`jnl`	The rate of convergence of AdaBoost. J. Mach. Learn. Res. Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire
2013	J	`jnl`	Toward a Theory of Pattern Discovery. CoRR Jonathan H. Huggins, Cynthia Rudin
2012	—	`conf`	An Integer Optimization Approach to Associative Classification. NIPS Allison Chang, Dimitris Bertsimas, Cynthia Rudin
2012	J	`jnl`	How to reverse-engineer quality rankings. Mach. Learn. Allison Chang, Cynthia Rudin, Michael Cavaretta, Robert Thomas, Gloria Chou
2012	J	`jnl`	Learning to Predict the Wisdom of Crowds CoRR Seyda Ertekin, Haym Hirsh, Cynthia Rudin
2012	J	`jnl`	Machine Learning for theNew York City Power Grid. IEEE Trans. Pattern Anal. Mach. Intell. Cynthia Rudin, David L. Waltz, Roger Anderson, Albert Boulanger, Ansaf Salleb-Aouissi, Maggie Chow, Haimonti Dutta, Philip Gross, Bert Huang, Steve Ierome
2012	A*	`conf`	Open Problem: Does AdaBoost Always Cycle? COLT Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies
2012	C	`conf`	Progressive Clustering with Learned Seeds: An Event Categorization System for Power Grid. SEKE Boyi Xie, Rebecca J. Passonneau, Haimonti Dutta, Jing-Yeu Miaw, Axinia Radeva, Ashish Tomar, Cynthia Rudin
2012	—	`conf`	Selective Sampling of Labelers for Approximating the Crowd. AAAI Fall Symposium: Machine Aggregation of Human Judgment Seyda Ertekin, Haym Hirsh, Cynthia Rudin
2012	Misc	`conf`	The Influence of Operational Cost on Estimation. ISAIM Cynthia Rudin, Theja Tulabandhula
2011	J	`jnl`	21st-Century Data Miners Meet 19th-Century Electrical Cables. Computer Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Steve Ierome, Delfina Isaac
2011	J	`jnl`	Machine Learning and the Traveling Repairman CoRR Theja Tulabandhula, Cynthia Rudin, Patrick Jaillet
2011	J	`jnl`	Machine Learning with Operational Costs CoRR Theja Tulabandhula, Cynthia Rudin
2011	J	`jnl`	On Equivalence Relationships Between Classification and Ranking Algorithms. J. Mach. Learn. Res. Seyda Ertekin, Cynthia Rudin
2011	A*	`conf`	Sequential Event Prediction with Association Rules. COLT Cynthia Rudin, Benjamin Letham, Ansaf Salleb-Aouissi, Eugene Kogan, David Madigan
2011	—	`conf`	The Machine Learning and Traveling Repairman Problem. ADT Theja Tulabandhula, Cynthia Rudin, Patrick Jaillet
2011	J	`jnl`	The Rate of Convergence of AdaBoost CoRR Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire
2011	A*	`conf`	The Rate of Convergence of Adaboost. COLT Indraneel Mukherjee, Cynthia Rudin, Robert E. Schapire
2010	J	`jnl`	A process for predicting manhole events in Manhattan. Mach. Learn. Cynthia Rudin, Rebecca J. Passonneau, Axinia Radeva, Haimonti Dutta, Steve Ierome, Delfina Isaac
2009	J	`jnl`	Margin-based Ranking and an Equivalence between AdaBoost and RankBoost. J. Mach. Learn. Res. Cynthia Rudin, Robert E. Schapire
2009	—	`conf`	Online coordinate boosting. ICCV Workshops Raphael Pelossof, Michael Jones, Ilia Vovsha, Cynthia Rudin
2009	C	`conf`	Reducing Noise in Labels and Features for a Real World Dataset: Application of NLP Corpus Annotation Methods. CICLing Rebecca J. Passonneau, Cynthia Rudin, Axinia Radeva, Zhi An Liu
2009	C	`conf`	Report Cards for Manholes: Eliciting Expert Feedback for a Learning Task. ICMLA Axinia Radeva, Cynthia Rudin, Rebecca J. Passonneau, Delfina Isaac
2009	J	`jnl`	The P-Norm Push: A Simple Convex Ranking Algorithm that Concentrates at the Top of the List. J. Mach. Learn. Res. Cynthia Rudin
2008	—	`conf`	Arabic Morphological Tagging, Diacritization, and Lemmatization Using Lexeme Models and Feature Ranking. ACL (2) Ryan Roth, Owen Rambow, Nizar Habash, Mona T. Diab, Cynthia Rudin
2006	A*	`conf`	Ranking with a P-Norm Push. COLT Cynthia Rudin
2005	A*	`conf`	Margin-Based Ranking Meets Boosting in the Middle. COLT Cynthia Rudin, Corinna Cortes, Mehryar Mohri, Robert E. Schapire
2005	J	`jnl`	Stability Analysis for Regularized Least Squares Regression CoRR Cynthia Rudin
2004	A*	`conf`	Boosting Based on a Smooth Margin. COLT Cynthia Rudin, Robert E. Schapire, Ingrid Daubechies
2004	J	`jnl`	The Dynamics of AdaBoost: Cyclic Behavior and Convergence of Margins. J. Mach. Learn. Res. Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire
2003	—	`conf`	On the Dynamics of Boosting. NIPS Cynthia Rudin, Ingrid Daubechies, Robert E. Schapire