Timezone: »
Shapley value is a classic notion from game theory, historically used to quantify the contributions of individuals within groups, and more recently applied to assign values to data points when training machine learning models. Despite its foundational role, a key limitation of the data Shapley framework is that it only provides valuations for points within a fixed data set. It does not account for statistical aspects of the data and does not give a way to reason about points outside the data set. To address these limitations, we propose a novel framework -- distributional Shapley -- where the value of a point is defined in the context of an underlying data distribution. We prove that distributional Shapley has several desirable statistical properties; for example, the values are stable under perturbations to the data points themselves and to the underlying data distribution. We leverage these properties to develop a new algorithm for estimating values from data, which comes with formal guarantees and runs two orders of magnitude faster than state-of-the-art algorithms for computing the (non-distributional) data Shapley values. We apply distributional Shapley to diverse data sets and demonstrate its utility in a data market setting.
Author Information
Amirata Ghorbani (Stanford)
Michael Kim (Stanford University)
James Zou (Stanford University)
More from the Same Authors
-
2021 : Meaningfully Explaining a Model's Mistakes »
· Abubakar Abid · James Zou -
2021 : Meaningfully Explaining a Model's Mistakes »
Abubakar Abid · James Zou -
2021 : MetaDataset: A Dataset of Datasets for Evaluating Distribution Shifts and Training Conflicts »
Weixin Liang · James Zou · Weixin Liang -
2021 : Have the Cake and Eat It Too? Higher Accuracy and Less Expense when Using Multi-label ML APIs Online »
Lingjiao Chen · James Zou · Matei Zaharia -
2021 : Machine Learning API Shift Assessments: Change is Coming! »
Lingjiao Chen · James Zou · Matei Zaharia -
2021 : Do Humans Trust Advice More if it Comes from AI? An Analysis of Human-AI Interactions »
Kailas Vodrahalli · James Zou -
2022 : On the nonlinear correlation of ML performance across data subpopulations »
Weixin Liang · Yining Mao · Yongchan Kwon · Xinyu Yang · James Zou -
2022 : GSCLIP : A Framework for Explaining Distribution Shifts in Natural Language »
Zhiying Zhu · Weixin Liang · James Zou -
2022 : Evaluation of ML in Health/Science »
James Zou -
2022 : Data Sculpting: Interpretable Algorithm for End-to-End Cohort Selection »
Ruishan Liu · James Zou -
2022 : Data Budgeting for Machine Learning »
Weixin Liang · James Zou -
2022 Poster: When and How Mixup Improves Calibration »
Linjun Zhang · Zhun Deng · Kenji Kawaguchi · James Zou -
2022 Poster: Efficient Online ML API Selection for Multi-Label Classification Tasks »
Lingjiao Chen · Matei Zaharia · James Zou -
2022 Poster: Improving Out-of-Distribution Robustness via Selective Augmentation »
Huaxiu Yao · Yu Wang · Sai Li · Linjun Zhang · Weixin Liang · James Zou · Chelsea Finn -
2022 Spotlight: Efficient Online ML API Selection for Multi-Label Classification Tasks »
Lingjiao Chen · Matei Zaharia · James Zou -
2022 Spotlight: Improving Out-of-Distribution Robustness via Selective Augmentation »
Huaxiu Yao · Yu Wang · Sai Li · Linjun Zhang · Weixin Liang · James Zou · Chelsea Finn -
2022 Spotlight: When and How Mixup Improves Calibration »
Linjun Zhang · Zhun Deng · Kenji Kawaguchi · James Zou -
2021 Poster: Improving Generalization in Meta-learning via Task Augmentation »
Huaxiu Yao · Long-Kai Huang · Linjun Zhang · Ying WEI · Li Tian · James Zou · Junzhou Huang · Zhenhui (Jessie) Li -
2021 Spotlight: Improving Generalization in Meta-learning via Task Augmentation »
Huaxiu Yao · Long-Kai Huang · Linjun Zhang · Ying WEI · Li Tian · James Zou · Junzhou Huang · Zhenhui (Jessie) Li -
2021 Poster: How to Learn when Data Reacts to Your Model: Performative Gradient Descent »
Zachary Izzo · Lexing Ying · James Zou -
2021 Spotlight: How to Learn when Data Reacts to Your Model: Performative Gradient Descent »
Zachary Izzo · Lexing Ying · James Zou -
2019 Poster: Concrete Autoencoders: Differentiable Feature Selection and Reconstruction »
Muhammed Fatih Balın · Abubakar Abid · James Zou -
2019 Poster: Discovering Conditionally Salient Features with Statistical Guarantees »
Jaime Roquero Gimenez · James Zou -
2019 Oral: Discovering Conditionally Salient Features with Statistical Guarantees »
Jaime Roquero Gimenez · James Zou -
2019 Oral: Concrete Autoencoders: Differentiable Feature Selection and Reconstruction »
Muhammed Fatih Balın · Abubakar Abid · James Zou -
2019 Poster: Data Shapley: Equitable Valuation of Data for Machine Learning »
Amirata Ghorbani · James Zou -
2019 Oral: Data Shapley: Equitable Valuation of Data for Machine Learning »
Amirata Ghorbani · James Zou -
2018 Poster: Multicalibration: Calibration for the (Computationally-Identifiable) Masses »
Ursula Hebert-Johnson · Michael Kim · Omer Reingold · Guy Rothblum -
2018 Oral: Multicalibration: Calibration for the (Computationally-Identifiable) Masses »
Ursula Hebert-Johnson · Michael Kim · Omer Reingold · Guy Rothblum -
2018 Poster: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions »
Kevin Tian · Teng Zhang · James Zou -
2018 Oral: CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions »
Kevin Tian · Teng Zhang · James Zou