Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning

2026-02-01

AI Summary

This work addresses the challenge in evidential deep learning of disentangling epistemic and aleatoric uncertainty under distributional shift, where standard approaches often exhibit overconfidence on out-of-distribution samples. The authors propose DIP-EDL, a novel method that explicitly decouples class prediction from uncertainty magnitude through a density-aware pseudo-count mechanism, modeling the conditional label distribution and marginal covariate density separately. Built upon a hierarchical Bayesian framework, DIP-EDL integrates amortized variational inference with Dirichlet parameterization to achieve, for the first time in evidential deep learning, asymptotically identifiable separation of the two uncertainty types. Experiments demonstrate that DIP-EDL significantly improves calibration, robustness, and interpretability on out-of-distribution data while preserving strong predictive performance in high-density regions.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks. Despite its popularity, its theoretical foundations and behavior under distributional shift remain poorly understood. In this work, we provide a principled statistical interpretation by proving that EDL training corresponds to amortized variational inference in a hierarchical Bayesian model with a tempered pseudo-likelihood. This perspective reveals a major drawback: standard EDL conflates epistemic and aleatoric uncertainty, leading to systematic overconfidence on out-of-distribution (OOD) inputs. To address this, we introduce Density-Informed Pseudo-count EDL (DIP-EDL), a new parametrization that decouples class prediction from the magnitude of uncertainty by separately estimating the conditional label distribution and the marginal covariate density. This separation preserves evidence in high-density regions while shrinking predictions toward a uniform prior for OOD data. Theoretically, we prove that DIP-EDL achieves asymptotic concentration. Empirically, we show that our method enhances interpretability and improves robustness and uncertainty calibration under distributional shift.

Problem

• Evidential Deep Learning
• distributional shift
• out-of-distribution detection
• uncertainty calibration
• epistemic uncertainty

Innovation

• Evidential Deep Learning
• Uncertainty Calibration
• Density-Informed Pseudo-Counts
• Distributional Shift
• Bayesian Inference