Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation

11 March 2022

Eyke Hüllermeier

Papers citing "Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation"

28 / 28 papers shown

Title
Position: Epistemic Artificial Intelligence is Essential for Machine Learning Models to Know When They Do Not Know Shireen Kudukkil Manchingal Fabio Cuzzolin 49 0 0 08 May 2025
An Axiomatic Assessment of Entropy- and Variance-based Uncertainty Quantification in Regression Christopher Bülte Yusuf Sale Timo Löhr Paul Hofman Gitta Kutyniok Eyke Hüllermeier UD 63 0 0 25 Apr 2025
Disentangling Uncertainties by Learning Compressed Data Representation Zhiyu An Zhibo Hou Wan Du UQCV UD 71 0 0 20 Mar 2025
Conformal Prediction and Human Decision Making Jessica Hullman Yifan Wu Dawei Xie Ziyang Guo Andrew Gelman 39 0 0 12 Mar 2025
Evidential Uncertainty Probes for Graph Neural Networks Linlin Yu Kangshuo Li Pritom Kumar Saha Yifei Lou Feng Chen EDL UQCV 82 0 0 11 Mar 2025
A calibration test for evaluating set-based epistemic uncertainty representations Mira Jürgens Thomas Mortier Eyke Hüllermeier Viktor Bengs Willem Waegeman 39 0 0 22 Feb 2025
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition Arthur Hoarau Benjamin Quost Sébastien Destercke Willem Waegeman UQCV UD PER 66 0 0 30 Jan 2025
Conformalized Credal Regions for Classification with Ambiguous Ground Truth Michele Caprio David Stutz Shuo Li Arnaud Doucet UQCV 64 4 0 07 Nov 2024
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks C. Damke Eyke Hüllermeier BDL 32 0 0 06 Sep 2024
On the Calibration of Epistemic Uncertainty: Principles, Paradoxes and Conflictual Loss Mohammed Fellaji Frédéric Pennerath Brieuc Conan-Guez Miguel Couceiro UQCV 43 1 0 16 Jul 2024
Linear Opinion Pooling for Uncertainty Quantification on Graphs C. Damke Eyke Hüllermeier 36 1 0 06 Jun 2024
Uncertainty-aware Evaluation of Auxiliary Anomalies with the Expected Anomaly Posterior Lorenzo Perini Maja R. Rudolph Sabrina Schmedding Chen Qiu 63 1 0 22 May 2024
Quantifying Aleatoric and Epistemic Uncertainty with Proper Scoring Rules Paul Hofman Yusuf Sale Eyke Hüllermeier UQCV UD PER 43 5 0 18 Apr 2024
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty Changbin Li Kangshuo Li Yuzhe Ou Lance M. Kaplan A. Jøsang Jin-Hee Cho Dong Hyun. Jeong Feng Chen UQCV BDL EDL 30 5 0 17 Apr 2024
Conformalized Credal Set Predictors Alireza Javanmardi David Stutz Eyke Hüllermeier 24 11 0 16 Feb 2024
Is Epistemic Uncertainty Faithfully Represented by Evidential Deep Learning Methods? Mira Jürgens Nis Meinert Viktor Bengs Eyke Hüllermeier Willem Waegeman UQCV UD PER EDL BDL 27 11 0 14 Feb 2024
Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? Maohao Shen Jeonghun Ryu Soumya Ghosh Yuheng Bu P. Sattigeri Subhro Das Greg Wornell EDL BDL UQCV 28 2 0 09 Feb 2024
Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks Nikita Kotelevskii Samuel Horváth Karthik Nandakumar Martin Takáč Maxim Panov UQCV FedML OOD 26 7 0 18 Dec 2023
Second-Order Uncertainty Quantification: A Distance-Based Approach Yusuf Sale Viktor Bengs Michele Caprio Eyke Hüllermeier PER UQCV UD 22 18 0 02 Dec 2023
Robust Statistical Comparison of Random Variables with Locally Varying Scale of Measurement Christoph Jansen G. Schollmeyer Hannah Blocher Julian Rodemann Thomas Augustin 25 14 0 22 Jun 2023
Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty? Yusuf Sale Michele Caprio Eyke Hüllermeier UD 18 25 0 16 Jun 2023
Proper Scoring Rules for Survival Analysis H. Yanagisawa 34 6 0 01 May 2023
Uncertainty Estimation by Fisher Information-based Evidential Deep Learning Danruo Deng Guangyong Chen Yang Yu Fu-Lun Liu Pheng-Ann Heng EDL UQCV FedML 27 40 0 03 Mar 2023
Variational Inference on the Final-Layer Output of Neural Networks Yadi Wei R. Khardon BDL UQCV 16 0 0 05 Feb 2023
On Second-Order Scoring Rules for Epistemic Uncertainty Quantification Viktor Bengs Eyke Hüllermeier Willem Waegeman UQCV 202 25 0 30 Jan 2023
The Unreasonable Effectiveness of Deep Evidential Regression N. Meinert J. Gawlikowski Alexander Lavin UQCV EDL 175 35 0 20 May 2022
Classifier Calibration: A survey on how to assess and improve predicted class probabilities Telmo de Menezes e Silva Filho Hao Song Miquel Perelló Nieto Raúl Santos-Rodríguez Meelis Kull Peter A. Flach 32 77 0 20 Dec 2021
DEUP: Direct Epistemic Uncertainty Prediction Salem Lahlou Moksh Jain Hadi Nekoei V. Butoi Paul Bertin Jarrid Rector-Brooks Maksym Korablyov Yoshua Bengio PER UQLM UQCV UD 202 81 0 16 Feb 2021