Learning Optimized Risk Scores

1 October 2016

Papers citing "Learning Optimized Risk Scores"

49 / 49 papers shown

Title
Fast and Interpretable Mortality Risk Scores for Critical Care Patients Chloe Qinyu Zhu Muhang Tian Lesia Semenova Jiachang Liu Jack Xu Joseph Scarpa Cynthia Rudin 92 4 0 21 Nov 2023
Fairness in Contextual Resource Allocation Systems: Metrics and Incompatibility Results Nathanael Jo Bill Tang Kathryn Dullerud S. Aghaei Eric Rice P. Vayanos 65 8 0 04 Dec 2022
TimberTrek: Exploring and Curating Sparse Decision Trees with Interactive Visualization Zijie J. Wang Chudi Zhong Rui Xin Takuya Takagi Zhi Chen Duen Horng Chau Cynthia Rudin Margo Seltzer 68 16 0 19 Sep 2022
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction Vinith Suriyakumar Marzyeh Ghassemi Berk Ustun 111 9 0 04 Jun 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 94 12 0 13 May 2022
Shapley variable importance clouds for interpretable machine learning Yilin Ning M. Ong Bibhas Chakraborty B. Goldstein Daniel Ting Roger Vaughan Nan Liu FAtt 72 74 0 06 Oct 2021
Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies F. Xie Han Yuan Yilin Ning M. Ong Mengling Feng Wynne Hsu B. Chakraborty Nan Liu 99 90 0 21 Jul 2021
A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanations Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang 76 41 0 04 Jun 2021
Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization M. Afnan Cynthia Rudin Vincent Conitzer J. Savulescu Abhishek Mishra Yanhe Liu M. Afnan SyDa 51 17 0 30 Apr 2021
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges Cynthia Rudin Chaofan Chen Zhi Chen Haiyang Huang Lesia Semenova Chudi Zhong FaML AI4CE LRM 258 678 0 20 Mar 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 79 23 0 14 Feb 2021
Deep Cox Mixtures for Survival Regression Chirag Nagpal Steve Yadlowsky Negar Rostamzadeh Katherine A. Heller CML 170 62 0 16 Jan 2021
When Does Uncertainty Matter?: Understanding the Impact of Predictive Uncertainty in ML Assisted Decision Making S. McGrath Parth Mehta Alexandra Zytek Isaac Lage Himabindu Lakkaraju UD 70 26 0 12 Nov 2020
Explainable Machine Learning for Public Policy: Use Cases, Gaps, and Research Directions Kasun Amarasinghe Kit Rodolfa Hemank Lamba Rayid Ghani ELM XAI 191 53 0 27 Oct 2020
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 105 88 0 08 May 2020
MonoNet: Towards Interpretable Models by Learning Monotonic Features An-phi Nguyen María Rodríguez Martínez FAtt 60 13 0 30 Sep 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 118 55 0 24 Aug 2019
Optimal Sparse Decision Trees Xiyang Hu Cynthia Rudin Margo Seltzer 143 175 0 29 Apr 2019
Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions Hao Wang Berk Ustun Flavio du Pin Calmon FaML 123 85 0 29 Jan 2019
Rank-one Convexification for Sparse Regression Alper Atamtürk A. Gómez 220 50 0 29 Jan 2019
Interpretable Optimal Stopping D. Ciocan V. Mišić 71 44 0 18 Dec 2018
An Interpretable Model with Globally Consistent Explanations for Credit Risk Chaofan Chen Kangcheng Lin Cynthia Rudin Yaron Shaposhnik Sijia Wang Tong Wang FAtt 87 94 0 30 Nov 2018
This Looks Like That: Deep Learning for Interpretable Image Recognition Chaofan Chen Oscar Li Chaofan Tao A. Barnett Jonathan Su Cynthia Rudin 338 1,193 0 27 Jun 2018
Direct Learning to Rank and Rerank Cynthia Rudin Yining Wang 51 10 0 21 Feb 2018
Learning Credible Models Jiaxuan Wang Jeeheh Oh Haozhu Wang Jenna Wiens FaML 94 30 0 08 Nov 2017
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions Oscar Li Hao Liu Chaofan Chen Cynthia Rudin 213 594 0 13 Oct 2017
An Optimization Approach to Learning Falling Rule Lists Chaofan Chen Cynthia Rudin 86 39 0 06 Oct 2017
On Fairness and Calibration Geoff Pleiss Manish Raghavan Felix Wu Jon M. Kleinberg Kilian Q. Weinberger FaML 232 886 0 06 Sep 2017
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 137 195 0 06 Apr 2017
Simple rules for complex decisions Jongbin Jung Connor Concannon Ravi Shroff Sharad Goel D. Goldstein CML 85 105 0 15 Feb 2017
European Union regulations on algorithmic decision-making and a "right to explanation" B. Goodman Seth Flaxman FaML AILaw 85 1,908 0 28 Jun 2016
Satisfying Real-world Goals with Dataset Constraints Gabriel Goh Andrew Cotter Maya R. Gupta M. Friedlander OffRL 88 215 0 24 Jun 2016
Concrete Problems in AI Safety Dario Amodei C. Olah Jacob Steinhardt Paul Christiano John Schulman Dandelion Mané 327 2,407 0 21 Jun 2016
Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model Benjamin Letham Cynthia Rudin Tyler H. McCormick D. Madigan FAtt 117 748 0 05 Nov 2015
Piecewise-Linear Approximation for Feature Subset Selection in a Sequential Logit Model Toshiki Sato Yuichi Takano Ryuhei Miyashiro 46 27 0 19 Oct 2015
Fairness Constraints: Mechanisms for Fair Classification Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 214 49 0 19 Jul 2015
Best Subset Selection via a Modern Optimization Lens Dimitris Bertsimas Angela King Rahul Mazumder 469 666 0 11 Jul 2015
Monotonic Calibrated Interpolated Look-Up Tables Maya R. Gupta Andrew Cotter Jan Pfeifer Konstantin Voevodski K. Canini Alexander Mangylov Wojtek Moczydlowski A. V. Esbroeck 230 129 0 23 May 2015
Interpretable Classification Models for Recidivism Prediction J. Zeng Berk Ustun Cynthia Rudin FaML 145 247 0 26 Mar 2015
Supersparse Linear Integer Models for Optimized Medical Scoring Systems Berk Ustun Cynthia Rudin 147 354 0 15 Feb 2015
Certifying and removing disparate impact Michael Feldman Sorelle A. Friedler John Moeller C. Scheidegger Suresh Venkatasubramanian FaML 268 2,001 0 11 Dec 2014
Falling Rule Lists Fulton Wang Cynthia Rudin 77 258 0 21 Nov 2014
Box Drawings for Learning with Imbalanced Data Siong Thye Goh Cynthia Rudin 97 43 0 13 Mar 2014
Binary Classifier Calibration: Non-parametric approach Mahdi Pakdaman Naeini G. Cooper Milos Hauskrecht 123 17 0 14 Jan 2014
Binary Classifier Calibration: Bayesian Non-Parametric Approach Mahdi Pakdaman Naeini G. Cooper Milos Hauskrecht 64 2 0 13 Jan 2014
Concentration inequalities for sampling without replacement Rémi Bardenet Odalric-Ambrym Maillard 131 167 0 16 Sep 2013
Supersparse Linear Integer Models for Predictive Scoring Systems Berk Ustun Stefano Tracà Cynthia Rudin 79 20 0 25 Jun 2013
Predicting accurate probabilities with a ranking loss A. Menon Xiaoqian Jiang Shankar Vembu Charles Elkan L. Ohno-Machado 120 72 0 18 Jun 2012
Composite Binary Losses Mark D. Reid Robert C. Williamson 170 223 0 17 Dec 2009