Papers Data Valuation
“Data Valuation” 태그가 달린 논문 119편 · 필터 해제
Semivalue-based data valuation is arbitrary and gameable
The game-theoretic notion of the semivalue offers a popular framework for credit attribution and data valuation in machine learning. Semivalues have been proposed for a variety of high-stakes decisions involving data, su…
Data ValuationFast-DataShapley: Neural Modeling for Training Data Valuation
The value and copyright of training data are crucial in the artificial intelligence industry. Service platforms should protect data providers' legitimate rights and fairly reward them for their contributions. Shapley val…
Data ValuationFaithful Group Shapley Value
Data Shapley is an important tool for data valuation, which quantifies the contribution of individual data points to machine learning models. In practice, group-level data valuation is desirable when data providers contr…
Computational EfficiencyData ValuationLosing is for Cherishing: Data Valuation Based on Machine Unlearning and Shapley Value
The proliferation of large models has intensified the need for efficient data valuation methods to quantify the contribution of individual data providers. Traditional approaches, such as game-theory-based Shapley value a…
Data ValuationMachine UnlearningEfficient Data Valuation Approximation in Federated Learning: A Sampling-based Approach
Federated learning paradigm to utilize datasets across multiple data providers. In FL, cross-silo data providers often hesitate to share their high-quality dataset unless their data value can be fairly assessed. Shapley …
Data ValuationFederated LearningFrom Fairness to Truthfulness: Rethinking Data Valuation Design
As large language models increasingly rely on external data sources, fairly compensating data contributors has become a central concern. In this paper, we revisit the design of data markets through a game-theoretic lens,…
Data ValuationFairnessMonte Carlo Sampling for Analyzing In-Context Examples
Prior works have shown that in-context learning is brittle to presentation factors such as the order, number, and choice of selected examples. However, ablation-based guidance on selecting the number of examples may igno…
Data ValuationIn-Context LearningShapley-Guided Utility Learning for Effective Graph Inference Data Valuation
Graph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes remains largely unexplored due to the chall…
Data ValuationValue predictionLLM-Aided Customizable Profiling of Code Data Based On Programming Language Concepts
Data profiling is critical in machine learning for generating descriptive statistics, supporting both deeper understanding and downstream tasks like data valuation and curation. This work addresses profiling specifically…
Code GenerationData ValuationDescriptiveFW-Shapley: Real-time Estimation of Weighted Shapley Values
Fair credit assignment is essential in various machine learning (ML) applications, and Shapley values have emerged as a valuable tool for this purpose. However, in critical ML applications such as data valuation and feat…
Data ValuationALinFiK: Learning to Approximate Linearized Future Influence Kernel for Scalable Third-Party LLM Data Valuation
Large Language Models (LLMs) heavily rely on high-quality training data, making data valuation crucial for optimizing model performance, especially when working within a limited budget. In this work, we aim to offer a th…
Data ValuationDUPRE: Data Utility Prediction for Efficient Data Valuation
Data valuation is increasingly used in machine learning (ML) to decide the fair compensation for data owners and identify valuable or harmful data for improving ML models. Cooperative game theory-based data valuation, su…
Data ValuationPredictionOptimizing Product Provenance Verification using Data Valuation Methods
Determining and verifying product provenance remains a critical challenge in global supply chains, particularly as geopolitical conflicts and shifting borders create new incentives for misrepresentation of commodities, s…
Data ValuationData Valuation using Neural Networks for Efficient Instruction Fine-Tuning
Influence functions provide crucial insights into model training, but existing methods suffer from large computational costs and limited generalization. Particularly, recent works have proposed various metrics and algori…
Data ValuationLiveVal: Time-aware Data Valuation via Adaptive Reference Points
Time-aware data valuation enhances training efficiency and model robustness, as early detection of harmful samples could prevent months of wasted computation. However, existing methods rely on model retraining or converg…
Data ValuationBeyond Models! Explainable Data Valuation and Metric Adaption for Recommendation
User behavior records serve as the foundation for recommender systems. While the behavior data exhibits ease of acquisition, it often suffers from varying quality. Current methods employ data valuation to discern high-qu…
Data ValuationFairnessModel OptimizationRecommendation SystemsOn the Impact of the Utility in Semivalue-based Data Valuation
Semivalue-based data valuation uses cooperative-game theory intuitions to assign each data point a value reflecting its contribution to a downstream task. Still, those values depend on the practitioner's choice of utilit…
Data ValuationvalidUnifying and Optimizing Data Values for Selection via Sequential-Decision-Making
Data selection has emerged as a crucial downstream application of data valuation. While existing data valuation methods have shown promise in selection tasks, the theoretical foundations and full potential of using data …
Data ValuationDecision MakingSequential Decision MakingQLESS: A Quantized Approach for Data Valuation and Selection in Large Language Model Fine-Tuning
Fine-tuning large language models (LLMs) is often constrained by the computational costs of processing massive datasets. We propose \textbf{QLESS} (Quantized Low-rank Gradient Similarity Search), which integrates gradien…
Data ValuationLanguage ModelingLanguage ModellingLarge Language Model+2Data Overvaluation Attack and Truthful Data Valuation in Federated Learning
In collaborative machine learning (CML), data valuation, i.e., evaluating the contribution of each client's data to the machine learning model, has become a critical task for incentivizing and selecting positive data con…
Data ValuationFederated Learning