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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 1–50 of 119 papers

TitleStatusHype
Semivalue-based data valuation is arbitrary and gameable—0
Fast-DataShapley: Neural Modeling for Training Data Valuation—0
Faithful Group Shapley ValueCode0
Losing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value—0
Efficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach—0
From Fairness to Truthfulness: Rethinking Data Valuation Design—0
Monte Carlo Sampling for Analyzing In-Context Examples—0
Shapley-Guided Utility Learning for Effective Graph Inference Data ValuationCode0
LLM-Aided Customizable Profiling of Code Data Based On Programming Language Concepts—0
FW-Shapley: Real-time Estimation of Weighted Shapley ValuesCode0
ALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data ValuationCode1
DUPRE: Data Utility Prediction for Efficient Data ValuationCode0
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
Private, Augmentation-Robust and Task-Agnostic Data Valuation Approach for Data Marketplace—0
Towards Data Valuation via Asymmetric Data ShapleyCode0
One Sample Fits All: Approximating All Probabilistic Values Simultaneously and EfficientlyCode0
Data Distribution ValuationCode0
shapiq: Shapley Interactions for Machine LearningCode4
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
2D-OOB: Attributing Data Contribution Through Joint Valuation FrameworkCode0
Uncertainty Quantification of Data Shapley via Statistical Inference—0
Personalization of Dataset Retrieval Results using a Metadata-based Data Valuation Method—0
In-Context Probing Approximates Influence Function for Data ValuationCode0
CHG Shapley: Efficient Data Valuation and Selection towards Trustworthy Machine LearningCode0
Towards Understanding the Influence of Training Samples on Explanations—0
Is Data Valuation Learnable and Interpretable?—0
Redefining Contributions: Shapley-Driven Federated LearningCode1
Scaling Laws for the Value of Individual Data Points in Machine LearningCode0
Proper Dataset Valuation by Pointwise Mutual Information—0
Data Valuation by Leveraging Global and Local Statistical Information—0
What is Your Data Worth to GPT? LLM-Scale Data Valuation with Influence FunctionsCode2
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