Novel Uncertainty Quantification Through Perturbation-Assisted Sample Synthesis.

Area of Science:

• Artificial Intelligence
• Statistics
• Data Science

Background:

• Uncertainty quantification is crucial for reliable decision-making in complex data scenarios.
• Deep learning models often struggle with uncertainty estimation, especially with unstructured data.
• Existing methods may lack statistical guarantees or require extensive data.

Purpose of the Study:

• To introduce a novel framework, Perturbation-Assisted Inference (PAI), for robust uncertainty quantification.
• To leverage synthetic data generation via Perturbation-Assisted Sample Synthesis (PASS) for enhanced data diversity and privacy.
• To ensure statistically guaranteed validity for inference, even without prior distributional knowledge.

Main Methods:

• PASS utilizes generative models with data perturbation to create synthetic data preserving rank properties.
• Knowledge transfer from pre-trained generative models improves distributional estimates.
• PAI ensures validity in pivotal inference and uses holdout samples for reliability in non-pivotal cases.

Main Results:

• PASS generates diverse, privacy-preserving synthetic data with enhanced estimation accuracy.
• PAI provides statistically guaranteed inference, improving conclusions without prior distributional knowledge.
• The framework demonstrates effectiveness across diverse applications including image synthesis and multimodal inference.

Conclusions:

• The PAI framework offers a statistically sound approach to uncertainty quantification in complex data environments.
• PASS effectively generates high-quality synthetic data, enhancing both data diversity and privacy.
• PAI shows broad applicability and effectiveness in advancing data-driven tasks requiring reliable uncertainty estimates.