Generalized Evidential Deep Learning: From a Bayesian Perspective

• URL: http://arxiv.org/abs/2605.25599v1
• Date: Mon, 25 May 2026 08:51:57 GMT
• Title: Generalized Evidential Deep Learning: From a Bayesian Perspective
• Abstract summary: Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation.<n>Generalized Evidential Deep Learning (GEDL) is a unified framework that disentangles the roles of individual components and systematically relates GEDL to existing variants.
• Score: 16.48788587754326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Evidential Deep Learning (EDL) has emerged as an efficient, sampling-free strategy for uncertainty estimation. A series of EDL variants have been proposed to address specific limitations of the original framework, achieving notable success. However, the underlying theoretical structure of EDL and the relationships among these variants have received limited systematic investigation. In this work, we establish a principled theoretical foundation for EDL by interpreting it within a generalized Bayesian framework that includes prior specification, posterior update, and training objective. We further characterize evidential uncertainty from a Bayesian distributional uncertainty viewpoint, established via asymptotic analysis. Building on this perspective, we further propose Generalized Evidential Deep Learning (GEDL), a unified and extensible framework that explicitly disentangles the roles of individual components and systematically relates GEDL to existing variants. Extensive experiments demonstrate that GEDL yields comparable results on classification, uncertainty estimation and OOD detections, with theoretical grounding.

Related papers

• Variational Inference for Evidential Deep Learning [69.9353638875714]
  We propose a mathematically principled framework, Variational Inference Evidential Deep Learning (VI-EDL)<n>VI-EDL achieves state-of-the-art performance in out-of-distribution detection, noise detection and autonomous driving scenario.
  arXiv  Detail & Related papers  (2026-05-26T02:32:59Z)
• On Multi-Step Theorem Prediction via Non-Parametric Structural Priors [50.16583672681106]
  In this work, we explore training-free theorem prediction through the lens of in-context learning (ICL)<n>We propose Theorem Precedence Graphs, which encode temporal dependencies from historical solution traces as directed graphs, and impose explicit topological constraints that effectively prune the search space during inference.<n>Experiments on the FormalGeo7k benchmark show that our method achieves 89.29% accuracy, substantially outperforming ICL baselines and matching state-of-the-art supervised models.
  arXiv  Detail & Related papers  (2026-03-05T06:08:50Z)
• Density-Informed Pseudo-Counts for Calibrated Evidential Deep Learning [1.9435397960631862]
  Evidential Deep Learning (EDL) is a popular framework for uncertainty-aware classification that models predictive uncertainty via Dirichlet distributions parameterized by neural networks.<n>We show that EDL training corresponds to amortized variational inference in a hierarchical Bayesian model with a tempered pseudo-likelihood.<n>We introduce Density-Informed Pseudo-count EDL (DIP-EDL), a new parametrization that decouples class prediction from the magnitude of uncertainty.
  arXiv  Detail & Related papers  (2026-02-01T22:57:39Z)
• Towards A Unified PAC-Bayesian Framework for Norm-based Generalization Bounds [63.47271262149291]
  We propose a unified framework for PAC-Bayesian norm-based generalization.<n>The key to our approach is a sensitivity matrix that quantifies the network outputs with respect to structured weight perturbations.<n>We derive a family of generalization bounds that recover several existing PAC-Bayesian results as special cases.
  arXiv  Detail & Related papers  (2026-01-13T00:42:22Z)
• The Procrustean Bed of Time Series: The Optimization Bias of Point-wise Loss [53.542743390809356]
  This paper aims to provide a first-principles analysis of the Expectation of Optimization Bias (EOB)<n>Our analysis reveals a fundamental paradigm paradox: the more deterministic and structured the time series, the more severe the bias by point-wise loss function.<n>We present a concrete solution that simultaneously achieves both principles via DFT or DWT.
  arXiv  Detail & Related papers  (2025-12-21T06:08:22Z)
• Bridging the Gap Between Bayesian Deep Learning and Ensemble Weather Forecasts [100.26854618129039]
  Weather forecasting is fundamentally challenged by the chaotic nature of the atmosphere.<n>Recent advances in Bayesian Deep Learning (BDL) offer a promising but often disconnected alternative.<n>We bridge these paradigms through a unified hybrid BDL framework for ensemble weather forecasting.
  arXiv  Detail & Related papers  (2025-11-18T07:49:52Z)
• PEPS: Quantum-Inspired Reinforcement Learning for Coherent Reasoning Traces in LLMs [0.0]
  Large Language Models (LLMs) often struggle with maintaining coherent multi-step reasoning traces.<n>This work introduces a quantum-inspired approach to address the challenge by incorporating a fidelity-based reward.<n>Results show that the proposed quantum-inspired approach offers significant improvements over supervised, contrastive, and pretrained baseline approaches.
  arXiv  Detail & Related papers  (2025-09-24T13:29:53Z)
• Revisiting Essential and Nonessential Settings of Evidential Deep Learning [70.82728812001807]
  Evidential Deep Learning (EDL) is an emerging method for uncertainty estimation. We propose Re-EDL, a simplified yet more effective variant of EDL.
  arXiv  Detail & Related papers  (2024-10-01T04:27:07Z)
• A Comprehensive Survey on Evidential Deep Learning and Its Applications [64.83473301188138]
  Evidential Deep Learning (EDL) provides reliable uncertainty estimation with minimal additional computation in a single forward pass. We first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We elaborate on its extensive applications across various machine learning paradigms and downstream tasks.
  arXiv  Detail & Related papers  (2024-09-07T05:55:06Z)
• Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? [35.15844215216846]
  EDL methods are trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function. Recent studies identify limitations of the existing methods to conclude their learned uncertainties are unreliable. We provide a sharper understanding of the behavior of a wide class of EDL methods by unifying various objective functions. We conclude that even when EDL methods are empirically effective on downstream tasks, this occurs despite their poor uncertainty quantification capabilities.
  arXiv  Detail & Related papers  (2024-02-09T03:23:39Z)
• Knowledge from Uncertainty in Evidential Deep Learning [10.751990848772028]
  This work reveals an evidential signal that emerges from the uncertainty value in Evidential Deep Learning (EDL) EDL is one example of a class of uncertainty-aware deep learning approaches designed to provide confidence about the current test sample.
  arXiv  Detail & Related papers  (2023-10-19T11:41:52Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.