Title: Filtering with Confidence: When Data Augmentation Meets Conformal Prediction
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: synthetic data; data augmentation; conformal prediction; conditional conformal prediction;

Abstract: With promising empirical performance across a wide range of applications, synthetic data augmentation appears a viable solution to data scarcity and  the demands of increasingly data-intensive models. Its effectiveness  lies in expanding the training set in a way that reduces estimator variance while introducing only minimal bias. Controlling this bias is therefore critical: effective data augmentation should generate diverse samples from the same underlying distribution as the training set, with minimal shifts. In this paper, we propose conformal data augmentation, a principled data filtering framework that leverages the power of conformal prediction to produce diverse synthetic data while filtering out poor-quality generations with provable risk control. Our method is simple to implement, requires no access to internal model logits, nor large-scale model retraining. We demonstrate the effectiveness of our approach across multiple tasks, including topic prediction, sentiment analysis, image classification, and fraud detection, showing consistent performance improvements of up to 40\% in $F_1$ score over unaugmented baselines, and 4\% over other filtered augmentation baselines.

TL;DR: We propose conformal data augmentation, a lightweight filtering method that keeps only high-quality synthetic samples with provable guarantee.