Calibration for the (Computationally-Identifiable) Masses

22 November 2017

Úrsula Hébert-Johnson

Papers citing "Calibration for the (Computationally-Identifiable) Masses"

24 / 24 papers shown

Title
Evaluating Model Performance Under Worst-case Subpopulations Mike Li Hongseok Namkoong Shangzhou Xia 48 17 0 01 Jul 2024
Low-Degree Multicalibration Parikshit Gopalan Michael P. Kim M. Singhal Shengjia Zhao FaML UQCV 24 37 0 02 Mar 2022
A comparison of approaches to improve worst-case predictive model performance over patient subpopulations Stephen R. Pfohl Haoran Zhang Yizhe Xu Agata Foryciarz Marzyeh Ghassemi N. Shah OOD 34 22 0 27 Aug 2021
Trustworthy AI: A Computational Perspective Haochen Liu Yiqi Wang Wenqi Fan Xiaorui Liu Yaxin Li Shaili Jain Yunhao Liu Anil K. Jain Jiliang Tang FaML 104 197 0 12 Jul 2021
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective Flavien Prost Pranjal Awasthi Nicholas Blumm A. Kumthekar Trevor Potter Li Wei Xuezhi Wang Ed H. Chi Jilin Chen Alex Beutel 50 15 0 20 May 2021
Local Calibration: Metrics and Recalibration Rachel Luo Aadyot Bhatnagar Yu Bai Shengjia Zhao Huan Wang Caiming Xiong Silvio Savarese Stefano Ermon Edward Schmerling Marco Pavone 27 14 0 22 Feb 2021
An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction Stephen R. Pfohl Agata Foryciarz N. Shah FaML 33 108 0 20 Jul 2020
Individual Calibration with Randomized Forecasting Shengjia Zhao Tengyu Ma Stefano Ermon 16 57 0 18 Jun 2020
A Notion of Individual Fairness for Clustering Matthäus Kleindessner Pranjal Awasthi Jamie Morgenstern FaML 42 30 0 08 Jun 2020
Review of Mathematical frameworks for Fairness in Machine Learning E. del Barrio Paula Gordaliza Jean-Michel Loubes FaML FedML 15 39 0 26 May 2020
A survey of bias in Machine Learning through the prism of Statistical Parity for the Adult Data Set Philippe C. Besse E. del Barrio Paula Gordaliza Jean-Michel Loubes Laurent Risser FaML 22 63 0 31 Mar 2020
Fair Active Learning Hadis Anahideh Abolfazl Asudeh Saravanan Thirumuruganathan FaML 46 51 0 06 Jan 2020
Maximum Weighted Loss Discrepancy Fereshte Khani Aditi Raghunathan Percy Liang 28 16 0 08 Jun 2019
Putting Fairness Principles into Practice: Challenges, Metrics, and Improvements Alex Beutel Jilin Chen Tulsee Doshi Hai Qian Allison Woodruff Christine Luu Pierre Kreitmann Jonathan Bischof Ed H. Chi FaML 30 150 0 14 Jan 2019
From Soft Classifiers to Hard Decisions: How fair can we be? R. Canetti A. Cohen Nishanth Dikkala Govind Ramnarayan Sarah Scheffler Adam D. Smith FaML 14 59 0 03 Oct 2018
Fairness Without Demographics in Repeated Loss Minimization Tatsunori B. Hashimoto Megha Srivastava Hongseok Namkoong Percy Liang FaML 19 575 0 20 Jun 2018
Fairness Under Composition Cynthia Dwork Christina Ilvento FaML 31 124 0 15 Jun 2018
Multiaccuracy: Black-Box Post-Processing for Fairness in Classification Michael P. Kim Amirata Ghorbani James Zou MLAU 25 336 0 31 May 2018
Probably Approximately Metric-Fair Learning G. Rothblum G. Yona FaML FedML 21 85 0 08 Mar 2018
Online Learning with an Unknown Fairness Metric Stephen Gillen Christopher Jung Michael Kearns Aaron Roth FaML 37 143 0 20 Feb 2018
Learning Adversarially Fair and Transferable Representations David Madras Elliot Creager T. Pitassi R. Zemel FaML 236 676 0 17 Feb 2018
A comparative study of fairness-enhancing interventions in machine learning Sorelle A. Friedler C. Scheidegger Suresh Venkatasubramanian Sonam Choudhary Evan P. Hamilton Derek Roth FaML 25 636 0 13 Feb 2018
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness Michael Kearns Seth Neel Aaron Roth Zhiwei Steven Wu FaML 48 770 0 14 Nov 2017
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments Alexandra Chouldechova FaML 207 2,092 0 24 Oct 2016