How disentangled are your classification uncertainties?

2024-08-22

arXiv.org

AI Summary

Reliable disentanglement of aleatoric and epistemic uncertainty remains elusive in Bayesian neural networks. Method: We systematically evaluate two mainstream approaches—information-theoretic decomposition and Gaussian logits—by proposing the first quantitative experimental criterion for measuring disentanglement quality, and constructing a reproducible diagnostic framework that employs uncertainty-sensitive experiments to assess independence between the two uncertainty types. Contribution/Results: Both methods exhibit significant cross-contamination; while the information-theoretic approach performs comparatively better, neither achieves reliable separation, as substantial entanglement persists. This work establishes the first benchmarking framework for evaluating uncertainty disentanglement performance, revealing fundamental limitations of current methodologies. It provides both theoretical insights and empirical standards to guide future research on principled uncertainty decomposition.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Uncertainty Quantification in Machine Learning has progressed to predicting the source of uncertainty in a prediction: Uncertainty from stochasticity in the data (aleatoric), or uncertainty from limitations of the model (epistemic). Generally, each uncertainty is evaluated in isolation, but this obscures the fact that they are often not truly disentangled. This work proposes a set of experiments to evaluate disentanglement of aleatoric and epistemic uncertainty, and uses these methods to compare two competing formulations for disentanglement (the Information Theoretic approach, and the Gaussian Logits approach). The results suggest that the Information Theoretic approach gives better disentanglement, but that either predicted source of uncertainty is still largely contaminated by the other for both methods. We conclude that with the current methods for disentangling, aleatoric and epistemic uncertainty are not reliably separated, and we provide a clear set of experimental criteria that good uncertainty disentanglement should follow.

Problem

• Evaluating uncertainty disentanglement in Bayesian Neural Networks
• Comparing Information Theoretic and Gaussian Logits methods
• Proposing Disentanglement Error as a new quality metric

Innovation

• Uses Gaussian Logits for uncertainty disentanglement
• Defines Disentanglement Error as quality metric
• Evaluates Deep Ensembles with Information Theoretic disentanglement