Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

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Deep Learning beta_shapley
Overview

Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning

This repository provides an implementation of the paper Beta Shapley: a Unified and Noise-reduced Data Valuation Framework for Machine Learning accepted at AISTATS 2022 as oral presentation. We propose a noise-reduced data valuation method, Beta Shapley, which is powerful at capturing the importance of data points.

Quick start

We provide a notebook using the Covertype dataset. It shows how to compute the Beta Shapley value and its application on several downstream ML tasks.

--> Beta Shapley can identify noisy samples by focusing marginal contributions on small cardinalities.

--> Beta Shapley on the CIFAR100 test dataset. Mislabeled data points have negative Beta Shapley values, meaning they actually harm the model performance. Beta Shapley can detect mislabeled points.

Files

betashap/ShapEngine.py: main class for computing Beta-Shapley.

betashap/data.py: handles loading and preprocessing datasets.

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Comments
  • Code For CIFAR&ResNet?

    Code For CIFAR&ResNet?

    Hello! Thank you for your interesting and enlightening work.

    Could you release the experimental code using CIFAR100 and ResNet-18 mentioned in the paper? Since the released code is mainly utilizing numpy and sklearn, adding this part is not simple for me.

    opened by brave-cattle 1
Owner
Yongchan Kwon
Yongchan Kwon
GitHub
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