Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and
Risk-sensitive Learning

Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning

19 October 2017

José Miguel Hernández-Lobato

Finale Doshi-Velez

Papers citing "Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning"

9 / 9 papers shown

Title
Token-Level Uncertainty Estimation for Large Language Model Reasoning Tunyu Zhang Haizhou Shi Yibin Wang Hengyi Wang Xiaoxiao He ... Ligong Han Kai Xu Huatian Zhang Dimitris N. Metaxas Hao Wang LRM 19 0 0 16 May 2025
Quantifying Aleatoric and Epistemic Uncertainty in Machine Learning: Are Conditional Entropy and Mutual Information Appropriate Measures? Lisa Wimmer Yusuf Sale Paul Hofman Bern Bischl Eyke Hüllermeier PER UD 42 65 0 07 Sep 2022
URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks Meet P. Vadera Adam D. Cobb B. Jalaeian Benjamin M. Marlin BDL UQCV 27 16 0 08 Jul 2020
Uncertainty in Gradient Boosting via Ensembles Aleksei Ustimenko Liudmila Prokhorenkova A. Malinin UQCV 28 94 0 18 Jun 2020
Generalized Bayesian Posterior Expectation Distillation for Deep Neural Networks Meet P. Vadera B. Jalaeian Benjamin M. Marlin BDL FedML UQCV 17 20 0 16 May 2020
Scaling active inference Alexander Tschantz Manuel Baltieri A. Seth Christopher L. Buckley BDL AI4CE 19 68 0 24 Nov 2019
Ensemble Distribution Distillation A. Malinin Bruno Mlodozeniec Mark Gales UQCV 27 231 0 30 Apr 2019
Trust Region Value Optimization using Kalman Filtering Shirli Di-Castro Shashua Shie Mannor 19 7 0 23 Jan 2019
Predictive Uncertainty Estimation via Prior Networks A. Malinin Mark Gales UD BDL EDL UQCV PER 32 898 0 28 Feb 2018