How disentangled are your classification uncertainties? (2408.12175v1)

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.

• The paper demonstrates that neither the IT nor the GL approach fully disentangles aleatoric and epistemic uncertainties in classification tasks.
• The analysis reveals that Deep Ensembles capture epistemic uncertainty more robustly compared to methods like MC-Dropout and GL, especially with varying dataset sizes.
• Experimental results from OoD and label noise tests expose spurious interactions, highlighting the need for improved uncertainty quantification frameworks.

Overview of "How Disentangled are Your Classification Uncertainties?"

The paper written by Ivo Pascal de Jong, Andreea Ioana Sburlea, and Matias Valdenegro-Toro, titled "How Disentangled are Your Classification Uncertainties?" explores the challenge of disentangling aleatoric and epistemic uncertainty in machine learning systems. The authors present a thorough examination of two distinct methodologies, the Information Theoretic (IT) approach and the Gaussian Logits (GL) approach, investigating how well these methods achieve true disentanglement of classification uncertainties.

Summary

Introduction

Uncertainty quantification (UQ) has become pivotal for reliable ML systems. It is critical to not only quantify the total uncertainty but also to identify its source: aleatoric uncertainty (inherent noise in data) or epistemic uncertainty (model-related uncertainty due to knowledge limitations). While numerous strategies exist for UQ, the separation of these uncertainties is not always well-established.

Background

The prevalent frameworks for disentangling uncertainties include Bayesian Neural Networks (BNNs) and specific disentanglement approaches like IT and GL. This paper aims to shed light on the effectiveness of the IT and GL approaches in truly separating the uncertainties.

Gaussian Logits Approach

The GL approach explicitly models aleatoric uncertainty in the architecture by having one output head predict the mean and another predict the variance. MC-Dropout, MC-DropConnect, Deep Ensembles, and Flipout are sampled to account for epistemic uncertainty.

Information Theoretic Approach

The IT approach approximates the Mutual Information (MI) between the prediction and model parameters to differentiate total uncertainty into aleatoric and epistemic components. This method relies heavily on the entropy of probability distributions and their expectations.

Experimental Design

To evaluate the disentanglement quality, the authors proposed three experiments:

1. Dataset Size Experiment: Varying the size of training data to manipulate epistemic uncertainty while aleatoric uncertainty remains constant.
2. Out-of-Distribution (OoD) Detection Experiment: Using samples from an unknown class to induce epistemic uncertainty while keeping aleatoric uncertainty unaffected.
3. Label Noise Experiment: Introducing label noise to increase aleatoric uncertainty without influencing epistemic uncertainty.

Results

Dataset Size Experiment

As expected, a reduction in the dataset size led to an increase in epistemic uncertainty. However, GL failed to consistently capture this behavior with MC-Dropout and MC-DropConnect, whereas Deep Ensembles showed robust performance. Moreover, an unanticipated increase in aleatoric uncertainty was observed across smaller datasets, highlighting interaction issues in current disentanglement methods.

OoD Detection Experiment

Aleatoric uncertainty unexpectedly increased for OoD samples, indicating that the current models could not isolate epistemic uncertainty in the presence of novel data. The ROC-AUC scores for separating OoD samples were higher than anticipated, again pointing to the conflation of uncertainties.

Label Noise Experiment

The introduction of label noise resulted in a consistent increase in aleatoric uncertainty across all methods. However, GL conflated aleatoric and epistemic uncertainty, evidenced by an increased epistemic uncertainty with added label noise. The IT approach fared better but still demonstrated some spurious interactions.

Implications and Future Directions

The research underscores the challenges inherent in accurately separating aleatoric and epistemic uncertainties using current methodologies. Misidentification of uncertainty sources can undermine downstream applications such as active learning, decision making, and model interpretation. Therefore, achieving more robust disentanglement methods is crucial. Future research should develop more reliable benchmarks and refine the conceptual frameworks to better isolate these uncertainty components.

Conclusion

This comprehensive examination establishes that while some progress has been made, existing methods do not yet robustly disentangle aleatoric and epistemic uncertainty. The novel experimental setups proposed serve as a benchmark for future advancements in the field.

By presenting concrete evaluations and identifying limitations in popular approaches, this paper significantly contributes to the ongoing discourse in uncertainty quantification. However, advancing towards more reliable disentanglement remains an open challenge and a critical avenue for future research in machine learning.