Amazing Things Come From Having Many Good Models

Amazing Things Come From Having Many Good Models

5 July 2024

Cynthia Rudin

Jon Donnelly

Papers citing "Amazing Things Come From Having Many Good Models"

17 / 17 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 62 3 0 21 Nov 2023
The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance J. Donnelly Srikar Katta Cynthia Rudin E. Browne FAtt 43 15 0 24 Sep 2023
When Do Neural Nets Outperform Boosted Trees on Tabular Data? Duncan C. McElfresh Sujay Khandagale Jonathan Valverde C. VishakPrasad Ben Feuer Chinmay Hegde Ganesh Ramakrishnan Micah Goldblum Colin White LMTD 64 158 0 04 May 2023
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 51 15 0 19 Sep 2022
Exploring the Whole Rashomon Set of Sparse Decision Trees Rui Xin Chudi Zhong Zhi Chen Takuya Takagi Margo Seltzer Cynthia Rudin 52 55 0 16 Sep 2022
Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations Tessa Han Suraj Srinivas Himabindu Lakkaraju FAtt 80 86 0 02 Jun 2022
Predictive Multiplicity in Probabilistic Classification J. Watson-Daniels David C. Parkes Berk Ustun 46 40 0 02 Jun 2022
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 68 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 204 671 0 20 Mar 2021
Characterizing Fairness Over the Set of Good Models Under Selective Labels Amanda Coston Ashesh Rambachan Alexandra Chouldechova FaML 72 85 0 02 Jan 2021
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy Kit T. Rodolfa Hemank Lamba Rayid Ghani 74 90 0 05 Dec 2020
Underspecification Presents Challenges for Credibility in Modern Machine Learning Alexander DÁmour Katherine A. Heller D. Moldovan Ben Adlam B. Alipanahi ... Kellie Webster Steve Yadlowsky T. Yun Xiaohua Zhai D. Sculley OffRL 109 687 0 06 Nov 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 74 86 0 08 May 2020
Predictive Multiplicity in Classification Charles Marx Flavio du Pin Calmon Berk Ustun 116 145 0 14 Sep 2019
On the Existence of Simpler Machine Learning Models Lesia Semenova Cynthia Rudin Ronald E. Parr 54 84 0 05 Aug 2019
Sanity Checks for Saliency Maps Julius Adebayo Justin Gilmer M. Muelly Ian Goodfellow Moritz Hardt Been Kim FAtt AAML XAI 127 1,966 0 08 Oct 2018
Machine Learning that Matters K. Wagstaff 116 313 0 18 Jun 2012