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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
Optimizing Product Provenance Verification using Data Valuation Methods—0
LiveVal: Time-aware Data Valuation via Adaptive Reference Points—0
Data Valuation using Neural Networks for Efficient Instruction Fine-TuningCode0
Beyond Models! Explainable Data Valuation and Metric Adaption for RecommendationCode0
On the Impact of the Utility in Semivalue-based Data Valuation—0
Unifying and Optimizing Data Values for Selection via Sequential-Decision-Making—0
QLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-TuningCode0
Data Overvaluation Attack and Truthful Data Valuation in Federated Learning—0
Fairshare Data Pricing via Data Valuation for Large Language Models—0
Fortifying Federated Learning Towards Trustworthiness via Auditable Data Valuation and Verifiable Client Contribution—0
Data value estimation on private gradients—0
LossVal: Efficient Data Valuation for Neural NetworksCode0
Data Acquisition for Improving Model Fairness using Reinforcement Learning—0
Dissecting Representation Misalignment in Contrastive Learning via Influence Function—0
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles—0
Towards Data Valuation via Asymmetric Data ShapleyCode0
Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace—0
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and EfficientlyCode0
Data Distribution ValuationCode0
Targeted synthetic data generation for tabular data via hardness characterizationCode0
Investigating Layer Importance in Large Language Models—0
Influence-based Attributions can be ManipulatedCode0
Exploiting the Data Gap: Utilizing Non-ignorable Missingness to Manipulate Model Learning—0
Reframing Data Value for Large Language Models Through the Lens of Plausibility—0
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation—0
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