Novel Uncertainty Quantification Through Perturbation-Assisted Sample Synthesis.

ABSTRACT: This paper introduces a novel Perturbation-Assisted Inference (PAI) framework utilizing synthetic data generated by the Perturbation-Assisted Sample Synthesis (PASS) method. The framework focuses on uncertainty quantification in complex data scenarios, particularly involving unstructured data while utilizing deep learning models. On one hand, PASS employs a generative model to create synthetic data that closely mirrors raw data while preserving its rank properties through data perturbation, thereby enhancing data diversity and bolstering privacy. By incorporating knowledge transfer from large pre-trained generative models, PASS enhances estimation accuracy, yielding refined distributional estimates of various statistics via Monte Carlo experiments. On the other hand, PAI boasts its statist

SUBMITTER: Liu Y 

PROVIDER: S-EPMC11499304 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature