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What is Flagged in Uncertainty Quantification? Latent Density Models for
  Uncertainty Categorization

What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization

11 July 2022
Hao Sun
B. V. Breugel
Jonathan Crabbé
Nabeel Seedat
M. Schaar
ArXivPDFHTML

Papers citing "What is Flagged in Uncertainty Quantification? Latent Density Models for Uncertainty Categorization"

9 / 9 papers shown
Title
Disentangling Uncertainties by Learning Compressed Data Representation
Disentangling Uncertainties by Learning Compressed Data Representation
Zhiyu An
Zhibo Hou
Wan Du
UQCV
UD
66
0
0
20 Mar 2025
Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
Finer Disentanglement of Aleatoric Uncertainty Can Accelerate Chemical Histopathology Imaging
Ji-Hun Oh
Kianoush Falahkheirkhah
Rohit Bhargava
50
0
0
27 Feb 2025
A Survey of Confidence Estimation and Calibration in Large Language
  Models
A Survey of Confidence Estimation and Calibration in Large Language Models
Jiahui Geng
Fengyu Cai
Yuxia Wang
Heinz Koeppl
Preslav Nakov
Iryna Gurevych
UQCV
23
52
0
14 Nov 2023
Accountability in Offline Reinforcement Learning: Explaining Decisions
  with a Corpus of Examples
Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples
Hao Sun
Alihan Huyuk
Daniel Jarrett
M. Schaar
OffRL
23
6
0
11 Oct 2023
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
RORL: Robust Offline Reinforcement Learning via Conservative Smoothing
Rui Yang
Chenjia Bai
Xiaoteng Ma
Zhaoran Wang
Chongjie Zhang
Lei Han
OffRL
16
74
0
06 Jun 2022
Training language models to follow instructions with human feedback
Training language models to follow instructions with human feedback
Long Ouyang
Jeff Wu
Xu Jiang
Diogo Almeida
Carroll L. Wainwright
...
Amanda Askell
Peter Welinder
Paul Christiano
Jan Leike
Ryan J. Lowe
OSLM
ALM
301
11,730
0
04 Mar 2022
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on
  Open Problems
Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems
Sergey Levine
Aviral Kumar
George Tucker
Justin Fu
OffRL
GP
321
1,662
0
04 May 2020
Simple and Scalable Predictive Uncertainty Estimation using Deep
  Ensembles
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Balaji Lakshminarayanan
Alexander Pritzel
Charles Blundell
UQCV
BDL
268
5,635
0
05 Dec 2016
Dropout as a Bayesian Approximation: Representing Model Uncertainty in
  Deep Learning
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Y. Gal
Zoubin Ghahramani
UQCV
BDL
247
9,042
0
06 Jun 2015
1