Cover Image

Guided Uncertainty Learning Using a Post-Hoc Evidential Meta-Model

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Barker, Charmaine, Bethell, Daniel, Gerasimou, Simos
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
by: Barker, Charmaine, et al.
Published: (2025)

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
by: Bethell, Daniel, et al.
Published: (2023)

Safe But Not Sorry: Reducing Over-Conservatism in Safety Critics via Uncertainty-Aware Modulation
by: Bethell, Daniel, et al.
Published: (2025)

Learning to Navigate Under Imperfect Perception: Conformalised Segmentation for Safe Reinforcement Learning
by: Bethell, Daniel, et al.
Published: (2025)

Safe Reinforcement Learning in Black-Box Environments via Adaptive Shielding
by: Bethell, Daniel, et al.
Published: (2024)

Learning Fairer Representations with FairVIC
by: Barker, Charmaine, et al.
Published: (2024)

Uncertainty Quantification for Deep Regression using Contextualised Normalizing Flows
by: Marco, Adriel Sosa, et al.
Published: (2025)

DeepKnowledge: Generalisation-Driven Deep Learning Testing
by: Missaoui, Sondess, et al.
Published: (2024)

Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation
by: Chun, Yongchan, et al.
Published: (2026)

Uncertainty Estimation by Flexible Evidential Deep Learning
by: Yoon, Taeseong, et al.
Published: (2025)

Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage?
by: Shen, Maohao, et al.
Published: (2024)

Efficient Post-Hoc Uncertainty Calibration via Variance-Based Smoothing
by: Denoodt, Fabian, et al.
Published: (2025)

Uncertainty Estimation by Density Aware Evidential Deep Learning
by: Yoon, Taeseong, et al.
Published: (2024)

Bi-level Meta-Policy Control for Dynamic Uncertainty Calibration in Evidential Deep Learning
by: Yang, Zhen, et al.
Published: (2025)

Uncertainty Regularized Evidential Regression
by: Ye, Kai, et al.
Published: (2024)

Evidential Uncertainty Probes for Graph Neural Networks
by: Yu, Linlin, et al.
Published: (2025)

Weakly-Supervised Residual Evidential Learning for Multi-Instance Uncertainty Estimation
by: Liu, Pei, et al.
Published: (2024)

Density-Ratio Losses for Post-Hoc Learning to Defer
by: Soen, Alexander, et al.
Published: (2026)

Detecting Misbehaviors of Large Vision-Language Models by Evidential Uncertainty Quantification
by: Huang, Tao, et al.
Published: (2026)

EviNAM: Intelligibility and Uncertainty via Evidential Neural Additive Models
by: Schleibaum, Sören, et al.
Published: (2026)

Principled Input-Output-Conditioned Post-Hoc Uncertainty Estimation for Regression Networks
by: Bramlage, Lennart, et al.
Published: (2025)

Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods?
by: Jürgens, Mira, et al.
Published: (2024)

Post-Hoc Split-Point Self-Consistency Verification for Efficient, Unified Quantification of Aleatoric and Epistemic Uncertainty in Deep Learning
by: Zhao, Zhizhong, et al.
Published: (2025)

Efficient Uncertainty in LLMs through Evidential Knowledge Distillation
by: Nemani, Lakshmana Sri Harsha, et al.
Published: (2025)

Post-Hoc Calibrated Anomaly Detection
by: Gloumeau, Sean
Published: (2025)

Echoes of Socratic Doubt: Embracing Uncertainty in Calibrated Evidential Reinforcement Learning
by: Stutts, Alex Christopher, et al.
Published: (2024)

Understanding Disparities in Post Hoc Machine Learning Explanation
by: Mhasawade, Vishwali, et al.
Published: (2024)

Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
by: Schreck, John S., et al.
Published: (2023)

Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
by: Li, Changbin, et al.
Published: (2024)

Post-Hoc Reversal: Are We Selecting Models Prematurely?
by: Ranjan, Rishabh, et al.
Published: (2024)

Evidential Uncertainty Sets in Deep Classifiers Using Conformal Prediction
by: Karimi, Hamed, et al.
Published: (2024)

ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification
by: Duan, Yuhan, et al.
Published: (2025)

ARC-STAR: Auditable Post-Hoc Correction for PDE Foundation Models
by: Li, Chengze, et al.
Published: (2026)

Post-Hoc Guidance for Consistency Models by Joint Flow Distribution Learning
by: Hsu, Chia-Hong, et al.
Published: (2026)

Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
by: Li, Yuzhu, et al.
Published: (2025)

Post-Hoc Uncertainty Quantification in Pre-Trained Neural Networks via Activation-Level Gaussian Processes
by: Bergna, Richard, et al.
Published: (2025)

Guiding Reinforcement Learning Using Uncertainty-Aware Large Language Models
by: Shoaeinaeini, Maryam, et al.
Published: (2024)

Evidential Deep Learning for Probabilistic Modelling of Extreme Storm Events
by: Khot, Ayush, et al.
Published: (2024)

Revisiting Uncertainty: On Evidential Learning for Partially Relevant Video Retrieval
by: Li, Jun, et al.
Published: (2026)

Perturb and Correct: Post-Hoc Ensembles using Affine Redundancy
by: Quint, Eleanor
Published: (2026)