Towards Efficient Data Valuation Based on the Shapley Value

Abstract

"How much is my data worth?" is an increasingly common question posed by organizations and individuals alike. An answer to this question could allow, for instance, fairly distributing profits among multiple data contributors and determining prospective compensation when data breaches happen. In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in cooperative game theory. The Shapley value defines a unique payoff scheme that satisfies many desiderata for the notion of data value. However, the Shapley value often requires exponential time to compute. To meet this challenge, we propose a repertoire of efficient algorithms for approximating the Shapley value. We also demonstrate the value of each training instance for various benchmark datasets.

Cited by in corpus (38)

• Improving Fairness for Data Valuation in Horizontal Federated Learning
• Training Data Influence Analysis and Estimation: A Survey
• Transparent Contribution Evaluation for Secure Federated Learning on Blockchain
• DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
• A Contribution-based Device Selection Scheme in Federated Learning
• A Distributional Framework for Data Valuation
• HYDRA: Hypergradient Data Relevance Analysis for Interpreting Deep Neural Networks
• Causative Cyberattacks on Online Learning-based Automated Demand Response Systems
• Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning
• FedCoin: A Peer-to-Peer Payment System for Federated Learning
• A Principled Approach to Data Valuation for Federated Learning
• Rewarding High-Quality Data via Influence Functions
• Online Class-Incremental Continual Learning with Adversarial Shapley Value
• FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning
• SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning
• Data Leverage: A Framework for Empowering the Public in its Relationship with Technology Companies
• Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation
• The Thousand Faces of Explainable AI Along the Machine Learning Life Cycle: Industrial Reality and Current State of Research
• DU-Shapley: A Shapley Value Proxy for Efficient Dataset Valuation
• A Unified Framework for Task-Driven Data Quality Management
• Replication-Robust Payoff-Allocation for Machine Learning Data Markets
• One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning
• On Convergence of Nearest Neighbor Classifiers over Feature Transformations
• On Blame Attribution for Accountable Multi-Agent Sequential Decision Making
• Comparing the Value of Labeled and Unlabeled Data in Method-of-Moments Latent Variable Estimation
• Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning
• Mapping the Potential and Pitfalls of "Data Dividends" as a Means of Sharing the Profits of Artificial Intelligence
• Data Appraisal Without Data Sharing
• Interpretable feature subset selection: A Shapley value based approach
• Data-Driven Methods for Balancing Fairness and Efficiency in Ride-Pooling
• Fair Multi-party Machine Learning -- a Game Theoretic approach
• Text Counterfactuals via Latent Optimization and Shapley-Guided Search
• Data Markets to support AI for All: Pricing, Valuation and Governance
• HERALD: An Annotation Efficient Method to Detect User Disengagement in Social Conversations
• Shapley values and machine learning to characterize metamaterials for seismic applications
• ModelPred: A Framework for Predicting Trained Model from Training Data
• Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
• Transformer Query-Target Knowledge Discovery (TEND): Drug Discovery from CORD-19