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ORCIDCynthia Rudin
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- affiliation: Duke University, USA
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2020 – today
- 2023
[i84]Quinn Lanners, Harsh Parikh, Alexander Volfovsky, Cynthia Rudin, David Page:
From Feature Importance to Distance Metric: An Almost Exact Matching Approach for Causal Inference. CoRR abs/2302.11715 (2023)- [i83]Zhi Chen, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Understanding and Exploring the Whole Set of Good Sparse Generalized Additive Models. CoRR abs/2303.16047 (2023) - [i82]Jiachang Liu, Sam Rosen, Chudi Zhong, Cynthia Rudin:
OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems. CoRR abs/2304.06686 (2023) - [i81]Zhi Chen, Sarah Tan, Urszula Chajewska, Cynthia Rudin, Rich Caruana:
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help? CoRR abs/2304.11749 (2023) - 2022
- [j45]Chaofan Chen
, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
A holistic approach to interpretability in financial lending: Models, visualizations, and summary-explanations. Decis. Support Syst. 152: 113647 (2022) - [j44]Tong Wang, Cynthia Rudin:
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effects. INFORMS J. Comput. 34(3): 1626-1643 (2022) - [c65]Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer:
Fast Sparse Decision Tree Optimization via Reference Ensembles. AAAI 2022: 9604-9613 - [c64]Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. AISTATS 2022: 9304-9333 - [c63]Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Mango I. Seltzer:
Fast optimization of weighted sparse decision trees for use in optimal treatment regimes and optimal policy design. CIKM Workshops 2022 - [c62]Lesia Semenova, Cynthia Rudin, Ronald Parr:
On the Existence of Simpler Machine Learning Models. FAccT 2022: 1827-1858 - [c61]Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. NeurIPS 2022 - [c60]Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. NeurIPS 2022 - [c59]Elita A. Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju:
Data poisoning attacks on off-policy policy evaluation methods. UAI 2022: 1264-1274 - [i80]Jiachang Liu, Chudi Zhong, Margo I. Seltzer, Cynthia Rudin:
Fast Sparse Classification for Generalized Linear and Additive Models. CoRR abs/2202.11389 (2022) - [i79]Harsh Parikh, Kentaro Hoffman, Haoqi Sun, Wendong Ge, Jin Jing, Rajesh Amerineni, Lin Liu, Jimeng Sun, Sahar Zafar, Aaron Struck, Alexander Volfovsky, Cynthia Rudin, M. Brandon Westover:
Why Interpretable Causal Inference is Important for High-Stakes Decision Making for Critically Ill Patients and How To Do It. CoRR abs/2203.04920 (2022) - [i78]Haiyang Huang, Zhi Chen, Cynthia Rudin:
SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation. CoRR abs/2204.10926 (2022) - [i77]Yishay Mansour, Michal Moshkovitz, Cynthia Rudin:
There is no Accuracy-Interpretability Tradeoff in Reinforcement Learning for Mazes. CoRR abs/2206.04266 (2022) - [i76]Rui Xin, Chudi Zhong, Zhi Chen, Takuya Takagi, Margo I. Seltzer, Cynthia Rudin:
Exploring the Whole Rashomon Set of Sparse Decision Trees. CoRR abs/2209.08040 (2022) - [i75]Zijie J. Wang, Chudi Zhong, Rui Xin, Takuya Takagi, Zhi Chen, Duen Horng Chau, Cynthia Rudin, Margo I. Seltzer:
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization. CoRR abs/2209.09227 (2022) - [i74]Jiachang Liu, Chudi Zhong, Boxuan Li, Margo I. Seltzer, Cynthia Rudin:
FasterRisk: Fast and Accurate Interpretable Risk Scores. CoRR abs/2210.05846 (2022) - [i73]Ali Behrouz, Mathias Lécuyer, Cynthia Rudin, Margo I. Seltzer:
Fast Optimization of Weighted Sparse Decision Trees for use in Optimal Treatment Regimes and Optimal Policy Design. CoRR abs/2210.06825 (2022) - [i72]Alina Jade Barnett, Zhicheng Guo, Jin Jing, Wendong Ge, Cynthia Rudin, M. Brandon Westover:
Mapping the Ictal-Interictal-Injury Continuum Using Interpretable Machine Learning. CoRR abs/2211.05207 (2022) - [i71]Rui Zhang, Rui Xin, Margo I. Seltzer, Cynthia Rudin:
Optimal Sparse Regression Trees. CoRR abs/2211.14980 (2022) - 2021
- [j43]Divya Koyyalagunta, Anna Sun, Rachel Lea Draelos, Cynthia Rudin:
Playing Codenames with Language Graphs and Word Embeddings. J. Artif. Intell. Res. 71: 319-346 (2021) - [j42]Stefano Tracà, Cynthia Rudin, Weiyu Yan:
Regulating Greed Over Time in Multi-Armed Bandits. J. Mach. Learn. Res. 22: 3:1-3:99 (2021) - [j41]Tianyu Wang, Marco Morucci, M. Usaid Awan, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. J. Mach. Learn. Res. 22: 31:1-31:41 (2021) - [j40]Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik:
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMap, and PaCMAP for Data Visualization. J. Mach. Learn. Res. 22: 201:1-201:73 (2021) - [j39]Beau Coker, Cynthia Rudin, Gary King:
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. Manag. Sci. 67(10): 6174-6197 (2021) - [j38]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat. Mach. Intell. 3(12): 1061-1070 (2021) - [j37]Jianyou Wang, Cynthia Rudin, Yuren Zhou, Christopher Suh, Xiaoxuan Zhang:
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. Trans. Assoc. Comput. Linguistics 9: 605-620 (2021) - [c58]Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. AIES 2021: 316-326 - [i70]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:
dame-flame: A Python Library Providing Fast Interpretable Matching for Causal Inference. CoRR abs/2101.01867 (2021) - [i69]Jianyou Wang, Xiaoxuan Zhang, Yuren Zhou, Christopher Suh, Cynthia Rudin:
There Once Was a Really Bad Poet, It Was Automated but You Didn't Know It. CoRR abs/2103.03775 (2021) - [i68]Cynthia Rudin, Chaofan Chen, Zhi Chen, Haiyang Huang, Lesia Semenova, Chudi Zhong:
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges. CoRR abs/2103.11251 (2021) - [i67]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
IAIA-BL: A Case-based Interpretable Deep Learning Model for Classification of Mass Lesions in Digital Mammography. CoRR abs/2103.12308 (2021) - [i66]Michael Anis Mihdi Afnan, Cynthia Rudin, Vincent Conitzer, Julian Savulescu, Abhishek Mishra, Yanhe Liu, Masoud Afnan:
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization. CoRR abs/2105.00060 (2021) - [i65]Divya Koyyalagunta, Anna Sun, Rachel Lea Draelos, Cynthia Rudin:
Playing Codenames with Language Graphs and Word Embeddings. CoRR abs/2105.05885 (2021) - [i64]Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations. CoRR abs/2106.02605 (2021) - [i63]Alex Oesterling, Angikar Ghosal, Haoyang Yu, Rui Xin, Yasa Baig, Lesia Semenova, Cynthia Rudin:
Multitask Learning for Citation Purpose Classification. CoRR abs/2106.13275 (2021) - [i62]Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y. Lo, Cynthia Rudin:
Interpretable Mammographic Image Classification using Cased-Based Reasoning and Deep Learning. CoRR abs/2107.05605 (2021) - [i61]Yunyao Zhu, Stephen Hahn, Simon Mak, Yue Jiang, Cynthia Rudin:
BacHMMachine: An Interpretable and Scalable Model for Algorithmic Harmonization for Four-part Baroque Chorales. CoRR abs/2109.07623 (2021) - [i60]Zhi Chen, Alexander C. Ogren, Chiara Daraio, L. Catherine Brinson, Cynthia Rudin:
How to See Hidden Patterns in Metamaterials with Interpretable Machine Learning. CoRR abs/2111.05949 (2021) - [i59]Hayden McTavish, Chudi Zhong, Reto Achermann, Ilias Karimalis, Jacques Chen, Cynthia Rudin, Margo I. Seltzer:
Fast Sparse Decision Tree Optimization via Reference Ensembles. CoRR abs/2112.00798 (2021) - 2020
- [j36]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:
AI reflections in 2019. Nat. Mach. Intell. 2(1): 2-9 (2020) - [j35]Zhi Chen, Yijie Bei, Cynthia Rudin:
Concept whitening for interpretable image recognition. Nat. Mach. Intell. 2(12): 772-782 (2020) - [j34]Jiayun Dong, Cynthia Rudin:
Exploring the cloud of variable importance for the set of all good models. Nat. Mach. Intell. 2(12): 810-824 (2020) - [c57]Jerry Liu, Nathan O'Hara, Alexander Rubin, Rachel Lea Draelos, Cynthia Rudin:
Metaphor Detection Using Contextual Word Embeddings From Transformers. Fig-Lang@ACL 2020: 250-255 - [c56]Hunter Gregory, Steven Li, Pouya Mohammadi, Natalie Tarn, Rachel Lea Draelos, Cynthia Rudin:
A Transformer Approach to Contextual Sarcasm Detection in Twitter. Fig-Lang@ACL 2020: 270-275 - [c55]M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. AISTATS 2020: 3252-3262 - [c54]Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin:
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CVPR 2020: 2434-2442 - [c53]Tianyu Wang, Weicheng Ye, Dawei Geng, Cynthia Rudin:
Towards Practical Lipschitz Bandits. FODS 2020: 129-138 - [c52]Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer:
Generalized and Scalable Optimal Sparse Decision Trees. ICML 2020: 6150-6160 - [c51]Tianyu Wang, Cynthia Rudin:
Bandits for BMO Functions. ICML 2020: 9996-10006 - [c50]Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. UAI 2020: 1089-1098 - [i58]Zhi Chen, Yijie Bei, Cynthia Rudin:
Concept Whitening for Interpretable Image Recognition. CoRR abs/2002.01650 (2020) - [i57]M. Usaid Awan, Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Almost-Matching-Exactly for Treatment Effect Estimation under Network Interference. CoRR abs/2003.00964 (2020) - [i56]Marco Morucci, Vittorio Orlandi, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Adaptive Hyper-box Matching for Interpretable Individualized Treatment Effect Estimation. CoRR abs/2003.01805 (2020) - [i55]Sachit Menon, Alexandru Damian, Shijia Hu, Nikhil Ravi, Cynthia Rudin:
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models. CoRR abs/2003.03808 (2020) - [i54]Caroline Wang, Bin Han, Bhrij Patel, Feroze Mohideen, Cynthia Rudin:
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction. CoRR abs/2005.04176 (2020) - [i53]Jimmy Lin, Chudi Zhong, Diane Hu, Cynthia Rudin, Margo I. Seltzer:
Generalized Optimal Sparse Decision Trees. CoRR abs/2006.08690 (2020) - [i52]Tianyu Wang, Cynthia Rudin:
Bandits for BMO Functions. CoRR abs/2007.08703 (2020) - [i51]Qinwen Huang, Ye Zhou, Xiaochen Du, Reed Chen, Jianyou Wang, Cynthia Rudin, Alberto Bartesaghi:
Cryo-ZSSR: multiple-image super-resolution based on deep internal learning. CoRR abs/2011.11020 (2020) - [i50]Yingfan Wang, Haiyang Huang, Cynthia Rudin, Yaron Shaposhnik:
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data Visualization. CoRR abs/2012.04456 (2020)
2010 – 2019
- 2019
- [j33]Gah-Yi Ban, Cynthia Rudin:
The Big Data Newsvendor: Practical Insights from Machine Learning. Oper. Res. 67(1): 90-108 (2019) - [j32]Berk Ustun, Cynthia Rudin:
Learning Optimized Risk Scores. J. Mach. Learn. Res. 20: 150:1-150:75 (2019) - [j31]Aaron Fisher, Cynthia Rudin, Francesca Dominici:
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. 20: 177:1-177:81 (2019) - [j30]Cynthia Rudin:
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1(5): 206-215 (2019) - [c49]Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost-Exact Matching for Causal Inference. AISTATS 2019: 2445-2453 - [c48]Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin:
Interpretable Image Recognition with Hierarchical Prototypes. HCOMP 2019: 32-40 - [c47]Cynthia Rudin:
Do Simpler Models Exist and How Can We Find Them? KDD 2019: 1-2 - [c46]Xiyang Hu, Cynthia Rudin, Margo I. Seltzer:
Optimal Sparse Decision Trees. NeurIPS 2019: 7265-7273 - [c45]Chaofan Chen, Oscar Li, Daniel Tao, Alina Barnett, Cynthia Rudin, Jonathan Su:
This Looks Like That: Deep Learning for Interpretable Image Recognition. NeurIPS 2019: 8928-8939 - [c44]Stefano Tracà, Weiyu Yan, Cynthia Rudin:
Reducing Exploration of Dying Arms in Mortal Bandits. UAI 2019: 156-163 - [c43]M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost Matching Exactly With Instrumental Variables. UAI 2019: 1116-1126 - [i49]Jiayun Dong, Cynthia Rudin:
Variable Importance Clouds: A Way to Explore Variable Importance for the Set of Good Models. CoRR abs/1901.03209 (2019) - [i48]Tianyu Wang, Dawei Geng, Cynthia Rudin:
A Practical Bandit Method with Advantages in Neural Network Tuning. CoRR abs/1901.09277 (2019) - [i47]Xiyang Hu, Cynthia Rudin, Margo I. Seltzer:
Optimal Sparse Decision Trees. CoRR abs/1904.12847 (2019) - [i46]Cynthia Rudin, David Carlson:
The Secrets of Machine Learning: Ten Things You Wish You Had Known Earlier to be More Effective at Data Analysis. CoRR abs/1906.01998 (2019) - [i45]Peter Hase, Chaofan Chen, Oscar Li, Cynthia Rudin:
Interpretable Image Recognition with Hierarchical Prototypes. CoRR abs/1906.10651 (2019) - [i44]M. Usaid Awan, Yameng Liu, Marco Morucci, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Interpretable Almost-Matching-Exactly With Instrumental Variables. CoRR abs/1906.11658 (2019) - [i43]Stefano Tracà, Cynthia Rudin, Weiyu Yan:
Reducing Exploration of Dying Arms in Mortal Bandits. CoRR abs/1907.02571 (2019) - [i42]Lesia Semenova, Cynthia Rudin:
A study in Rashomon curves and volumes: A new perspective on generalization and model simplicity in machine learning. CoRR abs/1908.01755 (2019) - 2018
- [j29]Cynthia Rudin, Berk Ustun:
Optimized Scoring Systems: Toward Trust in Machine Learning for Healthcare and Criminal Justice. Interfaces 48(5): 449-466 (2018) - [j28]Cynthia Rudin, Seyda Ertekin:
Learning customized and optimized lists of rules with mathematical programming. Math. Program. Comput. 10(4): 659-702 (2018) - [c42]Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin:
Deep Learning for Case-Based Reasoning Through Prototypes: A Neural Network That Explains Its Predictions. AAAI 2018: 3530-3537 - [c41]Chaofan Chen, Cynthia Rudin:
An Optimization Approach to Learning Falling Rule Lists. AISTATS 2018: 604-612 - [c40]Cynthia Rudin, Yining Wang:
Direct Learning to Rank And Rerank. AISTATS 2018: 775-783 - [c39]Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin:
New Techniques for Preserving Global Structure and Denoising With Low Information Loss in Single-Image Super-Resolution. CVPR Workshops 2018: 874-881 - [i41]Cynthia Rudin, Yining Wang:
Direct Learning to Rank and Rerank. CoRR abs/1802.07400 (2018) - [i40]Siong Thye Goh, Cynthia Rudin:
A Minimax Surrogate Loss Approach to Conditional Difference Estimation. CoRR abs/1803.03769 (2018) - [i39]Beau Coker, Cynthia Rudin, Gary King:
A Theory of Statistical Inference for Ensuring the Robustness of Scientific Results. CoRR abs/1804.08646 (2018) - [i38]Yijie Bei, Alexandru Damian, Shijia Hu, Sachit Menon, Nikhil Ravi, Cynthia Rudin:
New Techniques for Preserving Global Structure and Denoising with Low Information Loss in Single-Image Super-Resolution. CoRR abs/1805.03383 (2018) - [i37]Awa Dieng, Yameng Liu, Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky:
Collapsing-Fast-Large-Almost-Matching-Exactly: A Matching Method for Causal Inference. CoRR abs/1806.06802 (2018) - [i36]Chaofan Chen, Oscar Li, Alina Barnett, Jonathan Su, Cynthia Rudin:
This looks like that: deep learning for interpretable image recognition. CoRR abs/1806.10574 (2018) - [i35]Ramin Moghaddass, Cynthia Rudin:
Bayesian Patchworks: An Approach to Case-Based Reasoning. CoRR abs/1809.03541 (2018) - [i34]Cynthia Rudin, Caroline Wang, Beau Coker:
The age of secrecy and unfairness in recidivism prediction. CoRR abs/1811.00731 (2018) - [i33]John Benhart, Tianlin Duan, Peter Hase, Liuyi Zhu, Cynthia Rudin:
Shall I Compare Thee to a Machine-Written Sonnet? An Approach to Algorithmic Sonnet Generation. CoRR abs/1811.05067 (2018) - [i32]Cynthia Rudin:
Please Stop Explaining Black Box Models for High Stakes Decisions. CoRR abs/1811.10154 (2018) - [i31]Chaofan Chen, Kangcheng Lin, Cynthia Rudin, Yaron Shaposhnik, Sijia Wang, Tong Wang:
An Interpretable Model with Globally Consistent Explanations for Credit Risk. CoRR abs/1811.12615 (2018) - 2017
- [j27]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
A Bayesian Framework for Learning Rule Sets for Interpretable Classification. J. Mach. Learn. Res. 18: 70:1-70:37 (2017) - [j26]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists for Categorical Data. J. Mach. Learn. Res. 18: 234:1-234:78 (2017) - [c38]Himabindu Lakkaraju, Cynthia Rudin:
Learning Cost-Effective and Interpretable Treatment Regimes. AISTATS 2017: 166-175 - [c37]Hongyu Yang, Cynthia Rudin, Margo I. Seltzer:
Scalable Bayesian Rule Lists. ICML 2017: 3921-3930 - [c36]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists. KDD 2017: 35-44 - [c35]Berk Ustun, Cynthia Rudin:
Optimized Risk Scores. KDD 2017: 1125-1134 - [i30]Elaine Angelino, Nicholas Larus-Stone, Daniel Alabi, Margo I. Seltzer, Cynthia Rudin:
Learning Certifiably Optimal Rule Lists for Categorical Data. CoRR abs/1704.01701 (2017) - [i29]Sudeepa Roy, Cynthia Rudin, Alexander Volfovsky, Tianyu Wang:
FLAME: A Fast Large-scale Almost Matching Exactly Approach to Causal Inference. CoRR abs/1707.06315 (2017) - [i28]Chaofan Chen, Cynthia Rudin:
An optimization approach to learning falling rule lists. CoRR abs/1710.02572 (2017) - [i27]Oscar Li, Hao Liu, Chaofan Chen, Cynthia Rudin:
Deep Learning for Case-based Reasoning through Prototypes: A Neural Network that Explains its Predictions. CoRR abs/1710.04806 (2017) - [i26]Tong Wang, Cynthia Rudin:
Causal Rule Sets for Identifying Subgroups with Enhanced Treatment Effect. CoRR abs/1710.05426 (2017) - [i25]Fulton Wang, Cynthia Rudin:
Dimension Reduction for Robust Covariate Shift Correction. CoRR abs/1711.10938 (2017) - 2016
- [j25]Ramin Moghaddass, Cynthia Rudin, David Madigan:
The Factorized Self-Controlled Case Series Method: An Approach for Estimating the Effects of Many Drugs on Many Outcomes. J. Mach. Learn. Res. 17: 185:1-185:24 (2016) - [j24]Berk Ustun, Cynthia Rudin:
Supersparse linear integer models for optimized medical scoring systems. Mach. Learn. 102(3): 349-391 (2016) - [j23]William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, David J. Libon, Rodney Swenson, Catherine C. Price, Melissa Lamar, Dana L. Penney:
Learning classification models of cognitive conditions from subtle behaviors in the digital Clock Drawing Test. Mach. Learn. 102(3): 393-441 (2016) - [c34]Vikas K. Garg, Cynthia Rudin, Tommi S. Jaakkola:
CRAFT: ClusteR-specific Assorted Feature selecTion. AISTATS 2016: 305-313 - [c33]Tong Wang, Cynthia Rudin, Finale Doshi-Velez, Yimin Liu, Erica Klampfl, Perry MacNeille:
Bayesian Rule Sets for Interpretable Classification. ICDM 2016: 1269-1274 - [c32]Benjamin Letham, Lydia M. Letham, Cynthia Rudin:
Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts. KDD 2016: 1695-1704 - [i24]Hongyu Yang, Cynthia Rudin, Margo I. Seltzer:
Scalable Bayesian Rule Lists. CoRR abs/1602.08610 (2016) - [i23]William Souillard-Mandar, Randall Davis, Cynthia Rudin, Rhoda Au, Dana L. Penney:
Interpretable Machine Learning Models for the Digital Clock Drawing Test. CoRR abs/1606.07163 (2016) - [i22]Himabindu Lakkaraju, Cynthia Rudin:
Learning Cost-Effective Treatment Regimes using Markov Decision Processes. CoRR abs/1610.06972 (2016) - 2015
- [j22]Tong Wang, Cynthia Rudin, Daniel Wagner, Rich Sevieri:
Finding Patterns with a Rotten Core: Data Mining for Crime Series with Cores. Big Data 3(1): 3-21 (2015) - [j21]Seyda Ertekin, Cynthia Rudin:
A Bayesian Approach to Learning Scoring Systems. Big Data 3(4): 267-276 (2015) - [j20]Theja Tulabandhula, Cynthia Rudin:
Generalization bounds for learning with linear, polygonal, quadratic and conic side knowledge. Mach. Learn. 100(2-3): 183-216 (2015) - [c31]Fulton Wang, Cynthia Rudin:
Falling Rule Lists. AISTATS 2015 - [i21]