Who's responsible? Jointly quantifying the contribution of the learning
algorithm and training data

v1v2 (latest)

Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data

AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2019

9 October 2019

Amirata Ghorbani

ArXiv (abs)PDF HTML

Papers citing "Who's responsible? Jointly quantifying the contribution of the learning algorithm and training data"

8 / 8 papers shown

Title
A case for data valuation transparency via DValCards Keziah Naggita Julienne LaChance TDI 296 0 0 29 Jun 2025
LossVal: Efficient Data Valuation for Neural Networks Tim Wibiral Mohamed Karim Belaid Maximilian Rabus Ansgar Scherp TDI 494 1 0 05 Dec 2024
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation Jiachen T. Wang Yuqing Zhu Yu Wang R. Jia Prateek Mittal TDI 265 21 0 30 Aug 2023
2D-Shapley: A Framework for Fragmented Data ValuationInternational Conference on Machine Learning (ICML), 2023 Zhihong Liu H. Just Xiangyu Chang Xinyu Chen R. Jia TDI 188 11 0 18 Jun 2023
Shapley Based Residual Decomposition for Instance AnalysisInternational Conference on Machine Learning (ICML), 2023 Tommy Liu Amanda S. Barnard TDI 156 2 0 30 May 2023
Shapley-NAS: Discovering Operation Contribution for Neural Architecture SearchComputer Vision and Pattern Recognition (CVPR), 2022 Han Xiao Ziwei Wang Zhengbiao Zhu Jie Zhou Jiwen Lu TDI 151 49 0 20 Jun 2022
Data Banzhaf: A Robust Data Valuation Framework for Machine LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2022 Jiachen T. Wang R. Jia FedML TDI 566 135 0 30 May 2022
Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation Weixin Liang James Zou Zhou Yu ELM 160 36 0 21 May 2020