EcoVal: An Efficient Data Valuation Framework for Machine Learning

Główne pojęcia

Efficient data valuation framework EcoVal accelerates the process by clustering similar data points and propagating value among them.

Streszczenie

The content introduces EcoVal, an efficient data valuation framework for machine learning models. It addresses the challenges of traditional Shapley value-based methods by clustering similar data points and distributing value efficiently. The framework is validated through theoretical proof and empirical evaluation on MNIST, CIFAR10, and CIFAR100 datasets.
Abstract:

Quantifying data value crucial in ML workflow.
Existing Shapley frameworks computationally expensive.
Introduce EcoVal for fast data valuation.
Introduction:

Data valuation pivotal in ML and analytics.
Quality of data determines model effectiveness.
Motivation:

Existing Shapley frameworks computationally costly.
Inefficiencies lead to increased carbon footprint.
Our Contribution:

Two-step approach for cluster-level valuation.
Production function formulation for individual data value estimation.
Related Work:

Literature review of Shapley Value applications in economics and ML.
Preliminaries:

LOO Error and Shapley Value definitions explained.
Proposed Method:

Leave-cluster-out technique for cluster-level valuation.
Value propagation within a cluster using production functions.
Discussion: Comparison with Original Shapely:

Theoretical comparison between original Shapley and proposed method's error margin calculation provided.

Statystyki

Shapley value based frame-
works require considerable amount of repeated training of the model to obtain the Shapley value.
Existing Data Shapley based frameworks suffer from high computational cost.
EcoVal performs clustering to reduce total number of data points during training phase.

Cytaty

Kluczowe wnioski z

EcoVal

by Ayush K Taru... o arxiv.org 03-19-2024

https://arxiv.org/pdf/2402.09288.pdf

Głębsze pytania

How can EcoVal's efficiency impact large-scale machine learning projects

EcoVal's efficiency can have a significant impact on large-scale machine learning projects by reducing the computational cost and time required for data valuation. In traditional methods like Data Shapley, the need for multiple model training runs makes it computationally expensive, especially as the dataset size increases. EcoVal's clustering approach and production function formulation allow for faster estimation of data values without the need for repeated model training iterations. This efficiency enables quicker decision-making processes in machine learning initiatives, particularly when dealing with extensive datasets.

What are potential drawbacks or limitations of the EcoVal framework

While EcoVal offers notable advantages in terms of efficiency and speed in data valuation for machine learning models, there are potential drawbacks or limitations to consider. One limitation could be related to the accuracy of cluster-level valuations compared to individual point valuations. Clustering similar data points may lead to some loss of granularity in determining specific data values within each cluster. Additionally, the reliance on regression models for adjustment terms introduces potential errors that could affect the overall accuracy of data valuations.

How can the concept of production functions be further applied in the field of machine learning

The concept of production functions can be further applied in the field of machine learning to enhance understanding and optimization of model performance. By treating inputs (data) as factors contributing to output (model performance), production functions can help quantify how different types or quantities of input data impact model outcomes. This approach can provide insights into optimizing resource allocation, identifying key features or patterns that drive model performance, and improving overall efficiency in developing machine learning algorithms.