Uncertainty-Aware Data-Efficient AI: An Information-Theoretic Perspective

2025-12-04

AI Summary

AI systems in data-scarce domains—such as robotics, communications, and healthcare—suffer from limited generalization due to epistemic uncertainty. Method: We propose a unified uncertainty quantification framework integrating generalized Bayesian learning, post-Bayesian inference, and information-theoretic generalization bounds. We introduce novel information-theoretic measures to formally link dataset size with predictive uncertainty, yielding finite-sample statistical guarantees; further, we enhance robustness under small-sample regimes via synthetic data augmentation and conformal risk control. Contribution/Results: Experiments across diverse real-world few-shot tasks demonstrate significant improvements in prediction reliability and data efficiency. The framework achieves a principled unification of uncertainty modeling, theoretical analysis, and practical deployment—bridging the gap between foundational theory and empirical performance in low-data AI systems.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

In context-specific applications such as robotics, telecommunications, and healthcare, artificial intelligence systems often face the challenge of limited training data. This scarcity introduces epistemic uncertainty, i.e., reducible uncertainty stemming from incomplete knowledge of the underlying data distribution, which fundamentally limits predictive performance. This review paper examines formal methodologies that address data-limited regimes through two complementary approaches: quantifying epistemic uncertainty and mitigating data scarcity via synthetic data augmentation. We begin by reviewing generalized Bayesian learning frameworks that characterize epistemic uncertainty through generalized posteriors in the model parameter space, as well as ``post-Bayes'' learning frameworks. We continue by presenting information-theoretic generalization bounds that formalize the relationship between training data quantity and predictive uncertainty, providing a theoretical justification for generalized Bayesian learning. Moving beyond methods with asymptotic statistical validity, we survey uncertainty quantification methods that provide finite-sample statistical guarantees, including conformal prediction and conformal risk control. Finally, we examine recent advances in data efficiency by combining limited labeled data with abundant model predictions or synthetic data. Throughout, we take an information-theoretic perspective, highlighting the role of information measures in quantifying the impact of data scarcity.

Problem

• Addresses data scarcity in AI applications like robotics and healthcare
• Examines methods to quantify epistemic uncertainty from limited data
• Reviews synthetic data augmentation to improve predictive performance

Innovation

• Generalized Bayesian learning for epistemic uncertainty
• Information-theoretic bounds linking data and uncertainty
• Conformal prediction with finite-sample statistical guarantees