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Cynthia 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]