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On the Brittleness of Bayesian Inference

On the Brittleness of Bayesian Inference

28 August 2013
H. Owhadi
C. Scovel
T. Sullivan
ArXivPDFHTML

Papers citing "On the Brittleness of Bayesian Inference"

16 / 16 papers shown
Title
Multiclass classification utilising an estimated algorithmic probability
  prior
Multiclass classification utilising an estimated algorithmic probability prior
K. Dingle
Pau Batlle
H. Owhadi
24
4
0
14 Dec 2022
On the Sensitivity of Reward Inference to Misspecified Human Models
On the Sensitivity of Reward Inference to Misspecified Human Models
Joey Hong
Kush S. Bhatia
Anca Dragan
19
24
0
09 Dec 2022
Robust Expected Information Gain for Optimal Bayesian Experimental
  Design Using Ambiguity Sets
Robust Expected Information Gain for Optimal Bayesian Experimental Design Using Ambiguity Sets
Jinwook Go
T. Isaac
21
10
0
20 May 2022
Probabilistic learning constrained by realizations using a weak
  formulation of Fourier transform of probability measures
Probabilistic learning constrained by realizations using a weak formulation of Fourier transform of probability measures
Christian Soize
13
5
0
06 May 2022
Bayesian inference in Epidemics: linear noise analysis
Bayesian inference in Epidemics: linear noise analysis
S. Bronstein
Stefan Engblom
R. Marin
21
0
0
21 Mar 2022
Probabilistic learning inference of boundary value problem with
  uncertainties based on Kullback-Leibler divergence under implicit constraints
Probabilistic learning inference of boundary value problem with uncertainties based on Kullback-Leibler divergence under implicit constraints
Christian Soize
22
5
0
10 Feb 2022
The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks
The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks
Alexander Bastounis
A. Hansen
Verner Vlacic
AAML
OOD
32
28
0
13 Sep 2021
More Data Can Expand the Generalization Gap Between Adversarially Robust
  and Standard Models
More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models
Lin Chen
Yifei Min
Mingrui Zhang
Amin Karbasi
OOD
35
64
0
11 Feb 2020
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust
  between Human and Machine
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine
Weisi Guo
30
40
0
11 Nov 2019
What can be estimated? Identifiability, estimability, causal inference
  and ill-posed inverse problems
What can be estimated? Identifiability, estimability, causal inference and ill-posed inverse problems
Oliver J. Maclaren
R. Nicholson
23
32
0
04 Apr 2019
Kernel Flows: from learning kernels from data into the abyss
Kernel Flows: from learning kernels from data into the abyss
H. Owhadi
G. Yoo
18
88
0
13 Aug 2018
Bayesian Probabilistic Numerical Methods
Bayesian Probabilistic Numerical Methods
Jon Cockayne
Chris J. Oates
T. Sullivan
Mark Girolami
19
164
0
13 Feb 2017
Gamblets for opening the complexity-bottleneck of implicit schemes for
  hyperbolic and parabolic ODEs/PDEs with rough coefficients
Gamblets for opening the complexity-bottleneck of implicit schemes for hyperbolic and parabolic ODEs/PDEs with rough coefficients
H. Owhadi
Lei Zhang
AI4CE
18
69
0
24 Jun 2016
Towards Machine Wald
Towards Machine Wald
H. Owhadi
C. Scovel
TPM
22
42
0
10 Aug 2015
Qualitative Robustness in Bayesian Inference
Qualitative Robustness in Bayesian Inference
H. Owhadi
C. Scovel
45
26
0
14 Nov 2014
Gaussian Approximation of General Nonparametric Posterior Distributions
Gaussian Approximation of General Nonparametric Posterior Distributions
Zuofeng Shang
Guang Cheng
31
4
0
13 Nov 2014
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