You can improve an already-trained probabilistic circuit's robustness to data noise and distribution shifts by applying PeTeR as a post-training step, without the cost of retraining the entire model from scratch.
PeTeR is a post-training method that makes probabilistic circuits (models for computing probability distributions) more robust to noisy data and distribution shifts without retraining from scratch. It uses distributionally-robust optimization to protect against worst-case data variations, improving model reliability on real-world data.