  Data augmentation refers to a wide range of techniques for improving model
generalization by augmenting training examples. Oftentimes such methods require
domain knowledge about the dataset at hand, spawning a plethora of recent
literature surrounding automated techniques for data augmentation. In this work
we apply one such method, bilevel optimization, to tackle the problem of graph
classification on the ogbg-molhiv dataset. Our best performing augmentation
achieved a test ROCAUC score of 77.77 % with a GIN+virtual classifier, which
makes it the most effective augmenter for this classifier on the leaderboard.
This framework combines a GIN layer augmentation generator with a bias
transformation and outperforms the same classifier augmented using the
state-of-the-art FLAG augmentation.

Machine Learning;Artificial Intelligence