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Dangers of Bayesian Model Averaging under Covariate Shift

Dangers of Bayesian Model Averaging under Covariate Shift

22 June 2021
Pavel Izmailov
Patrick K. Nicholson
Sanae Lotfi
A. Wilson
    OOD
    UQCV
    BDL
ArXivPDFHTML

Papers citing "Dangers of Bayesian Model Averaging under Covariate Shift"

12 / 12 papers shown
Title
Improving robustness to corruptions with multiplicative weight
  perturbations
Improving robustness to corruptions with multiplicative weight perturbations
Trung Trinh
Markus Heinonen
Luigi Acerbi
Samuel Kaski
44
0
0
24 Jun 2024
Reparameterization invariance in approximate Bayesian inference
Reparameterization invariance in approximate Bayesian inference
Hrittik Roy
M. Miani
Carl Henrik Ek
Philipp Hennig
Marvin Pfortner
Lukas Tatzel
Søren Hauberg
BDL
47
8
0
05 Jun 2024
When are ensembles really effective?
When are ensembles really effective?
Ryan Theisen
Hyunsuk Kim
Yaoqing Yang
Liam Hodgkinson
Michael W. Mahoney
FedML
UQCV
35
15
0
21 May 2023
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Do Bayesian Neural Networks Need To Be Fully Stochastic?
Mrinank Sharma
Sebastian Farquhar
Eric T. Nalisnick
Tom Rainforth
BDL
23
52
0
11 Nov 2022
Towards out of distribution generalization for problems in mechanics
Towards out of distribution generalization for problems in mechanics
Lingxiao Yuan
Harold S. Park
Emma Lejeune
OOD
AI4CE
36
17
0
29 Jun 2022
Being a Bit Frequentist Improves Bayesian Neural Networks
Being a Bit Frequentist Improves Bayesian Neural Networks
Agustinus Kristiadi
Matthias Hein
Philipp Hennig
BDL
UQCV
23
15
0
18 Jun 2021
PAC$^m$-Bayes: Narrowing the Empirical Risk Gap in the Misspecified
  Bayesian Regime
PACm^mm-Bayes: Narrowing the Empirical Risk Gap in the Misspecified Bayesian Regime
Warren Morningstar
Alexander A. Alemi
Joshua V. Dillon
82
16
0
19 Oct 2020
There Are Many Consistent Explanations of Unlabeled Data: Why You Should
  Average
There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Ben Athiwaratkun
Marc Finzi
Pavel Izmailov
A. Wilson
202
243
0
14 Jun 2018
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
276
5,661
0
05 Dec 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
308
2,890
0
15 Sep 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
285
9,138
0
06 Jun 2015
MCMC using Hamiltonian dynamics
MCMC using Hamiltonian dynamics
Radford M. Neal
185
3,266
0
09 Jun 2012
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