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Beyond Marginal Uncertainty: How Accurately can Bayesian Regression
  Models Estimate Posterior Predictive Correlations?
v1v2 (latest)

Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?

6 November 2020
Simon Mahns
Shengyang Sun
Roger C. Grosse
    UQCV
ArXiv (abs)PDFHTML

Papers citing "Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?"

20 / 20 papers shown
Active Learning with Task-Driven Representations for Messy Pools
Active Learning with Task-Driven Representations for Messy Pools
Kianoosh Ashouritaklimi
Tom Rainforth
SSLCLL
470
0
0
29 Oct 2025
Prediction-Oriented Subsampling from Data Streams
Prediction-Oriented Subsampling from Data Streams
Benedetta Lavinia Mussati
Freddie Bickford-Smith
Tom Rainforth
Stephen J. Roberts
314
0
0
05 Aug 2025
Active Fine-Tuning of Multi-Task Policies
Active Fine-Tuning of Multi-Task Policies
Marco Bagatella
Jonas Hübotter
Georg Martius
Andreas Krause
609
0
0
07 Oct 2024
Making Better Use of Unlabelled Data in Bayesian Active Learning
Making Better Use of Unlabelled Data in Bayesian Active Learning
Freddie Bickford-Smith
Adam Foster
Tom Rainforth
381
10
0
26 Apr 2024
Active Few-Shot Fine-Tuning
Active Few-Shot Fine-Tuning
Jonas Hübotter
Bhavya Sukhija
Lenart Treven
Yarden As
Andreas Krause
484
4
0
13 Feb 2024
PICProp: Physics-Informed Confidence Propagation for Uncertainty
  Quantification
PICProp: Physics-Informed Confidence Propagation for Uncertainty QuantificationNeural Information Processing Systems (NeurIPS), 2023
Qianli Shen
Wai Hoh Tang
Zhun Deng
Apostolos F. Psaros
Kenji Kawaguchi
488
2
0
10 Oct 2023
Anchor Points: Benchmarking Models with Much Fewer Examples
Anchor Points: Benchmarking Models with Much Fewer ExamplesConference of the European Chapter of the Association for Computational Linguistics (EACL), 2023
Rajan Vivek
Kawin Ethayarajh
Diyi Yang
Douwe Kiela
ALM
356
54
0
14 Sep 2023
BatchGFN: Generative Flow Networks for Batch Active Learning
BatchGFN: Generative Flow Networks for Batch Active Learning
Shreshth A. Malik
Salem Lahlou
Andrew Jesson
Moksh Jain
Nikolay Malkin
T. Deleu
Yoshua Bengio
Y. Gal
AI4CE
237
4
0
26 Jun 2023
Prediction-Oriented Bayesian Active Learning
Prediction-Oriented Bayesian Active LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2023
Freddie Bickford-Smith
Andreas Kirsch
Sebastian Farquhar
Y. Gal
Adam Foster
Tom Rainforth
274
61
0
17 Apr 2023
CO-BED: Information-Theoretic Contextual Optimization via Bayesian
  Experimental Design
CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental DesignInternational Conference on Machine Learning (ICML), 2023
Desi R. Ivanova
Joel Jennings
Tom Rainforth
Cheng Zhang
Adam Foster
351
4
0
27 Feb 2023
Unifying Approaches in Active Learning and Active Sampling via Fisher
  Information and Information-Theoretic Quantities
Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities
Andreas Kirsch
Y. Gal
FedML
299
30
0
01 Aug 2022
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and
  Bootstrapping
Ensembles for Uncertainty Estimation: Benefits of Prior Functions and Bootstrapping
Vikranth Dwaracherla
Zheng Wen
Ian Osband
Xiuyuan Lu
S. Asghari
Benjamin Van Roy
UQCV
386
22
0
08 Jun 2022
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian
  Inference, Active Learning, and Active Sampling
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling
Andreas Kirsch
Jannik Kossen
Y. Gal
UQCVBDL
216
4
0
18 May 2022
Evaluating High-Order Predictive Distributions in Deep Learning
Evaluating High-Order Predictive Distributions in Deep LearningConference on Uncertainty in Artificial Intelligence (UAI), 2022
Ian Osband
Zheng Wen
S. Asghari
Vikranth Dwaracherla
Xiuyuan Lu
Benjamin Van Roy
180
13
0
28 Feb 2022
Benchmarking Uncertainty Quantification on Biosignal Classification
  Tasks under Dataset Shift
Benchmarking Uncertainty Quantification on Biosignal Classification Tasks under Dataset Shift
Tong Xia
Jing Han
Cecilia Mascolo
OOD
268
13
0
16 Dec 2021
The Neural Testbed: Evaluating Joint Predictions
The Neural Testbed: Evaluating Joint PredictionsNeural Information Processing Systems (NeurIPS), 2021
Ian Osband
Zheng Wen
S. Asghari
Vikranth Dwaracherla
Botao Hao
M. Ibrahimi
Dieterich Lawson
Xiuyuan Lu
Brendan O'Donoghue
Benjamin Van Roy
UQCV
319
25
0
09 Oct 2021
A framework for benchmarking uncertainty in deep regression
A framework for benchmarking uncertainty in deep regression
F. Schmähling
Jörg Martin
Clemens Elster
UQCV
166
9
0
10 Sep 2021
From Predictions to Decisions: The Importance of Joint Predictive
  Distributions
From Predictions to Decisions: The Importance of Joint Predictive Distributions
Zheng Wen
Ian Osband
Chao Qin
Xiuyuan Lu
M. Ibrahimi
Vikranth Dwaracherla
Mohammad Asghari
Benjamin Van Roy
UQCV
226
27
0
20 Jul 2021
Epistemic Neural Networks
Epistemic Neural NetworksNeural Information Processing Systems (NeurIPS), 2021
Ian Osband
Zheng Wen
M. Asghari
Vikranth Dwaracherla
M. Ibrahimi
Xiyuan Lu
Benjamin Van Roy
UQCVBDL
922
137
0
19 Jul 2021
Test Distribution-Aware Active Learning: A Principled Approach Against
  Distribution Shift and Outliers
Test Distribution-Aware Active Learning: A Principled Approach Against Distribution Shift and Outliers
Andreas Kirsch
Tom Rainforth
Y. Gal
OODTTA
258
27
0
22 Jun 2021
1
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