[Projektor-NeurIPS 2023] Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources
This repository is the official implementation of the "Performance Scaling via Optimal Transport: Enabling Data Selection from Partially Revealed Sources" (NeurIPS 2023).
We propose a performance estimator for a model trained on any data source composition given limited sample information. We further develop a novel optimal transport based scaling law to predict performance on larger scales, which effectively finds the optimal composition of data sources for any target data size.
For better understanding of applying projektor to data source selection and performance scaling, we have provided a tutorial Jupyter notebook projektor_cifar10_example.ipynb.
The datasets should be placed in the folder 'data'. Please download the necessary datasets, e.g. CIFAR10.
RJ and the ReDS lab acknowledge support through grants from the Amazon-Virginia Tech Initiative for Efficient and Robust Machine Learning, the National Science Foundation under Grant No. IIS-2312794, NSF IIS-2313130, NSF OAC-2239622, and the Commonwealth Cyber Initiative