Learning Optimized Risk Scores

1 October 2016

Papers citing "Learning Optimized Risk Scores"

22 / 22 papers shown

Title
Probabilistic Scoring Lists for Interpretable Machine Learning Jonas Hanselle Stefan Heid Zhigang Zeng Eyke Hüllermeier 39 0 0 31 Jul 2024
MINTY: Rule-based Models that Minimize the Need for Imputing Features with Missing Values Lena Stempfle Fredrik D. Johansson 35 2 0 23 Nov 2023
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 36 3 0 21 Nov 2023
An Interpretable Loan Credit Evaluation Method Based on Rule Representation Learner Zi-yu Chen Xiaomeng Wang Yuanjiang Huang Tao Jia 46 1 0 03 Apr 2023
FasterRisk: Fast and Accurate Interpretable Risk Scores Jiachang Liu Chudi Zhong Boxuan Li Margo Seltzer Cynthia Rudin 44 16 0 12 Oct 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 43 14 0 19 Sep 2022
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction Vinith Suriyakumar Marzyeh Ghassemi Berk Ustun 44 6 0 04 Jun 2022
Perspectives on Incorporating Expert Feedback into Model Updates Valerie Chen Umang Bhatt Hoda Heidari Adrian Weller Ameet Talwalkar 40 11 0 13 May 2022
Framework for Evaluating Faithfulness of Local Explanations S. Dasgupta Nave Frost Michal Moshkovitz FAtt 124 61 0 01 Feb 2022
Learning Optimal Predictive Checklists Haoran Zhang Q. Morris Berk Ustun Marzyeh Ghassemi 26 11 0 02 Dec 2021
Shapley variable importance clouds for interpretable machine learning Yilin Ning M. Ong Bibhas Chakraborty B. Goldstein Daniel Ting Roger Vaughan Nan Liu FAtt 37 69 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 32 84 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 24 41 0 04 Jun 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 59 655 0 20 Mar 2021
Connecting Interpretability and Robustness in Decision Trees through Separation Michal Moshkovitz Yao-Yuan Yang Kamalika Chaudhuri 33 22 0 14 Feb 2021
Deep Cox Mixtures for Survival Regression Chirag Nagpal Steve Yadlowsky Negar Rostamzadeh Katherine A. Heller CML 44 59 0 16 Jan 2021
In Pursuit of Interpretable, Fair and Accurate Machine Learning for Criminal Recidivism Prediction Caroline Linjun Wang Bin Han Bhrij Patel Cynthia Rudin FaML HAI 64 84 0 08 May 2020
MonoNet: Towards Interpretable Models by Learning Monotonic Features An-phi Nguyen María Rodríguez Martínez FAtt 18 13 0 30 Sep 2019
Fairness Warnings and Fair-MAML: Learning Fairly with Minimal Data Dylan Slack Sorelle A. Friedler Emile Givental FaML 32 54 0 24 Aug 2019
Learning Certifiably Optimal Rule Lists for Categorical Data E. Angelino Nicholas Larus-Stone Daniel Alabi Margo Seltzer Cynthia Rudin 62 195 0 06 Apr 2017
Simple rules for complex decisions Jongbin Jung Connor Concannon Ravi Shroff Sharad Goel D. Goldstein CML 27 104 0 15 Feb 2017
Fairness Constraints: Mechanisms for Fair Classification Muhammad Bilal Zafar Isabel Valera Manuel Gomez Rodriguez Krishna P. Gummadi FaML 114 49 0 19 Jul 2015