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Detecting and Mitigating Test-time Failure Risks via Model-agnostic
  Uncertainty Learning

Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning

9 September 2021
Preethi Lahoti
Krishna P. Gummadi
G. Weikum
ArXivPDFHTML

Papers citing "Detecting and Mitigating Test-time Failure Risks via Model-agnostic Uncertainty Learning"

3 / 3 papers shown
Title
Non-Invasive Fairness in Learning through the Lens of Data Drift
Non-Invasive Fairness in Learning through the Lens of Data Drift
Ke Yang
A. Meliou
24
0
0
30 Mar 2023
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
5,652
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
9,109
0
06 Jun 2015
1