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Data Valuation

Data valuation in machine learning tries to determine the worth of data, or data sets, for downstream tasks. Some methods are task-agnostic and consider datasets as a whole, mostly for decision making in data markets. These look at distributional distances between samples. More often, methods look at how individual points affect performance of specific machine learning models. They assign a scalar to each element of a training set which reflects its contribution to the final performance of some model trained on it. Some concepts of value depend on a specific model of interest, others are model-agnostic.

Concepts of the usefulness of a datum or its influence on the outcome of a prediction have a long history in statistics and ML, in particular through the notion of the influence function. However, it has only been recently that rigorous and practical notions of value for data, and in particular data-sets, have appeared in the ML literature, often based on concepts from collaborative game theory, but also from generalization estimates of neural networks, or optimal transport theory, among others.

Papers

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Showing 26–50 of 119 papers

TitleStatusHype
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms—0
Data Valuation by Leveraging Global and Local Statistical Information—0
Data Valuation for Medical Imaging Using Shapley Value: Application on A Large-scale Chest X-ray Dataset—0
Data Valuation for Offline Reinforcement Learning—0
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method—0
DAVED: Data Acquisition via Experimental Design for Data Markets—0
Investigating Layer Importance in Large Language Models—0
Is Data Valuation Learnable and Interpretable?—0
Data Acquisition for Improving Model Fairness using Reinforcement Learning—0
Fundamentals of Task-Agnostic Data Valuation—0
Data value estimation on private gradients—0
A Note on "Towards Efficient Data Valuation Based on the Shapley Value''—0
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation—0
Improving Fairness for Data Valuation in Horizontal Federated Learning—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
FedCCEA : A Practical Approach of Client Contribution Evaluation for Federated Learning—0
Cooperative IoT Data Sharing with Heterogeneity of Participants Based on Electricity Retail—0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach—0
Augment & Valuate : A Data Enhancement Pipeline for Data-Centric AI—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning—0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning—0
Fairness-Aware Data Valuation for Supervised Learning—0
A Unified Framework for Task-Driven Data Quality Management—0
Fairshare Data Pricing via Data Valuation for Large Language Models—0
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