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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 51–100 of 119 papers

TitleStatusHype
Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method—0
LIA: Privacy-Preserving Data Quality Evaluation in Federated Learning Using a Lazy Influence Approximation—0
Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace—0
Reframing Data Value for Large Language Models Through the Lens of Plausibility—0
Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits—0
Semivalue-based data valuation is arbitrary and gameable—0
Shapley Value on Probabilistic Classifiers—0
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning—0
Threshold KNN-Shapley: A Linear-Time and Privacy-Friendly Approach to Data Valuation—0
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective—0
Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling—0
Towards Understanding Data Values: Empirical Results on Synthetic Data—0
Proper Dataset Valuation by Pointwise Mutual Information—0
Uncertainty Quantification of Data Shapley via Statistical Inference—0
Unifying and Optimizing Data Values for Selection via Sequential-Decision-Making—0
Validation Free and Replication Robust Volume-based Data Valuation—0
VTruST: Controllable value function based subset selection for Data-Centric Trustworthy AI—0
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles—0
Data Valuation for Offline Reinforcement Learning—0
Data Valuation for Vertical Federated Learning: A Model-free and Privacy-preserving Method—0
Data value estimation on private gradients—0
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
Efficient Data Shapley for Weighted Nearest Neighbor Algorithms—0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach—0
Energy-Based Learning for Cooperative Games, with Applications to Valuation Problems in Machine Learning—0
Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning—0
Fairness-Aware Data Valuation for Supervised Learning—0
Fairshare Data Pricing via Data Valuation for Large Language Models—0
2D-OOB: Attributing Data Contribution Through Joint Valuation FrameworkCode0
Improving Cooperative Game Theory-based Data Valuation via Data Utility LearningCode0
ModelPred: A Framework for Predicting Trained Model from Training DataCode0
Data valuation: The partial ordinal Shapley value for machine learningCode0
Data Valuation using Neural Networks for Efficient Instruction Fine-TuningCode0
Data Valuation using Reinforcement LearningCode0
Data Valuation with Gradient SimilarityCode0
Data Selection for Fine-tuning Large Language Models Using Transferred Shapley ValuesCode0
Probably Approximate Shapley Fairness with Applications in Machine LearningCode0
DeRDaVa: Deletion-Robust Data Valuation for Machine LearningCode0
Profit Allocation for Federated LearningCode0
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-TuningCode0
DUPRE: Data Utility Prediction for Efficient Data ValuationCode0
EcoVal: An Efficient Data Valuation Framework for Machine LearningCode0
Beyond Models! Explainable Data Valuation and Metric Adaption for RecommendationCode0
LossVal: Efficient Data Valuation for Neural NetworksCode0
Efficient Task-Specific Data Valuation for Nearest Neighbor AlgorithmsCode0
Targeted synthetic data generation for tabular data via hardness characterizationCode0
Accelerated Shapley Value Approximation for Data EvaluationCode0
Exploring Data Redundancy in Real-world Image Classification through Data SelectionCode0
Variance reduced Shapley value estimation for trustworthy data valuationCode0
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