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Bayesian Deep Ensembles via the Neural Tangent Kernel
v1v2 (latest)

Bayesian Deep Ensembles via the Neural Tangent Kernel

11 July 2020
Bobby He
Balaji Lakshminarayanan
Yee Whye Teh
    BDLUQCV
ArXiv (abs)PDFHTML

Papers citing "Bayesian Deep Ensembles via the Neural Tangent Kernel"

50 / 85 papers shown
Title
Model Reprogramming Demystified: A Neural Tangent Kernel Perspective
Model Reprogramming Demystified: A Neural Tangent Kernel Perspective
Ming-Yu Chung
Jiashuo Fan
Hancheng Ye
Qinsi Wang
Wei-Chen Shen
Chia-Mu Yu
Pin-Yu Chen
Sy-Yen Kuo
32
1
0
31 May 2025
Universal Value-Function Uncertainties
Universal Value-Function Uncertainties
Moritz A. Zanger
Max Weltevrede
Yaniv Oren
Pascal R. van der Vaart
Caroline Horsch
Wendelin Bohmer
M. Spaan
OffRL
74
0
0
27 May 2025
TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation
TULiP: Test-time Uncertainty Estimation via Linearization and Weight Perturbation
Yuhui Zhang
Dongshen Wu
Yuichiro Wada
Takafumi Kanamori
OODD
243
1
0
22 May 2025
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'
Shireen Kudukkil Manchingal
Andrew Bradley
Julian F. P. Kooij
Keivan K1 Shariatmadar
Neil Yorke-Smith
Fabio Cuzzolin
171
1
0
08 May 2025
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
Uncertainty Quantification for Machine Learning in Healthcare: A Survey
L. J. L. Lopez
Shaza Elsharief
Dhiyaa Al Jorf
Firas Darwish
Congbo Ma
Farah E. Shamout
544
0
0
04 May 2025
Position: Enough of Scaling LLMs! Lets Focus on Downscaling
Position: Enough of Scaling LLMs! Lets Focus on Downscaling
Ayan Sengupta
Ayan Sengupta
Tanmoy Chakraborty
112
0
0
02 May 2025
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model
Moritz A. Zanger
Pascal R. van der Vaart
Wendelin Bohmer
M. Spaan
UQCVBDL
507
2
0
14 Mar 2025
Observation Noise and Initialization in Wide Neural Networks
Observation Noise and Initialization in Wide Neural Networks
Sergio Calvo-Ordoñez
Jonathan Plenk
Richard Bergna
Alvaro Cartea
Jose Miguel Hernandez-Lobato
Konstantina Palla
Kamil Ciosek
122
1
0
03 Feb 2025
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks
Kaizheng Wang
Keivan K1 Shariatmadar
Shireen Kudukkil Manchingal
Fabio Cuzzolin
David Moens
Hans Hallez
UQCVBDL
270
14
0
28 Jan 2025
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
DistPred: A Distribution-Free Probabilistic Inference Method for Regression and Forecasting
Daojun Liang
Haixia Zhang
Dongfeng Yuan
UQCV
146
0
0
08 Jan 2025
Epistemic Uncertainty and Observation Noise with the Neural Tangent
  Kernel
Epistemic Uncertainty and Observation Noise with the Neural Tangent Kernel
Sergio Calvo-Ordoñez
Konstantina Palla
Kamil Ciosek
71
1
0
06 Sep 2024
Continual learning with the neural tangent ensemble
Continual learning with the neural tangent ensemble
Ari S. Benjamin
Christian Pehle
Kyle Daruwalla
UQCV
141
1
0
30 Aug 2024
Low-Budget Simulation-Based Inference with Bayesian Neural Networks
Low-Budget Simulation-Based Inference with Bayesian Neural Networks
Arnaud Delaunoy
Maxence de la Brassinne Bonardeaux
S. Mishra-Sharma
Gilles Louppe
81
2
0
27 Aug 2024
Neural Lineage
Neural Lineage
Runpeng Yu
Xinchao Wang
102
4
0
17 Jun 2024
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Credal Wrapper of Model Averaging for Uncertainty Estimation in Classification
Kaizheng Wang
Fabio Cuzzolin
Keivan K1 Shariatmadar
David Moens
Hans Hallez
UQCVBDL
128
6
0
23 May 2024
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of
  Large Language Models
Gaussian Stochastic Weight Averaging for Bayesian Low-Rank Adaptation of Large Language Models
Emre Onal
Klemens Flöge
Emma Caldwell
A. Sheverdin
Vincent Fortuin
UQCVBDL
125
12
0
06 May 2024
PINNACLE: PINN Adaptive ColLocation and Experimental points selection
PINNACLE: PINN Adaptive ColLocation and Experimental points selection
Gregory Kang Ruey Lau
Apivich Hemachandra
See-Kiong Ng
K. H. Low
3DPC
121
20
0
11 Apr 2024
On Uncertainty Quantification for Near-Bayes Optimal Algorithms
On Uncertainty Quantification for Near-Bayes Optimal Algorithms
Ziyu Wang
Chris Holmes
UQCV
105
3
0
28 Mar 2024
A prediction rigidity formalism for low-cost uncertainties in trained
  neural networks
A prediction rigidity formalism for low-cost uncertainties in trained neural networks
Filippo Bigi
Sanggyu Chong
Michele Ceriotti
Federico Grasselli
75
6
0
04 Mar 2024
Active Few-Shot Fine-Tuning
Active Few-Shot Fine-Tuning
Jonas Hübotter
Bhavya Sukhija
Lenart Treven
Yarden As
Andreas Krause
104
1
0
13 Feb 2024
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou
Maria Skoularidou
Konstantina Palla
Laurence Aitchison
Julyan Arbel
...
David Rügamer
Yee Whye Teh
Max Welling
Andrew Gordon Wilson
Ruqi Zhang
UQCVBDL
148
35
0
01 Feb 2024
Generative Posterior Networks for Approximately Bayesian Epistemic
  Uncertainty Estimation
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation
Melrose Roderick
Felix Berkenkamp
Fatemeh Sheikholeslami
Zico Kolter
UQCV
34
0
0
29 Dec 2023
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
Alexander M¨ollers
Alexander Immer
Elvin Isufi
Vincent Fortuin
SSLBDLUQCV
123
1
0
30 Nov 2023
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Ming-Yu Chung
Sheng-Yen Chou
Chia-Mu Yu
Pin-Yu Chen
Sy-Yen Kuo
Tsung-Yi Ho
DD
159
7
0
28 Nov 2023
Decomposing Uncertainty for Large Language Models through Input
  Clarification Ensembling
Decomposing Uncertainty for Large Language Models through Input Clarification Ensembling
Bairu Hou
Yujian Liu
Kaizhi Qian
Jacob Andreas
Shiyu Chang
Yang Zhang
UDUQCVPER
98
65
0
15 Nov 2023
On permutation symmetries in Bayesian neural network posteriors: a
  variational perspective
On permutation symmetries in Bayesian neural network posteriors: a variational perspective
Simone Rossi
Ankit Singh
T. Hannagan
69
3
0
16 Oct 2023
PICProp: Physics-Informed Confidence Propagation for Uncertainty
  Quantification
PICProp: Physics-Informed Confidence Propagation for Uncertainty Quantification
Qianli Shen
Wai Hoh Tang
Zhun Deng
Apostolos F. Psaros
Kenji Kawaguchi
211
1
0
10 Oct 2023
Commutative Width and Depth Scaling in Deep Neural Networks
Commutative Width and Depth Scaling in Deep Neural Networks
Soufiane Hayou
83
2
0
02 Oct 2023
Towards Efficient and Trustworthy AI Through
  Hardware-Algorithm-Communication Co-Design
Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design
Yongchao Chen
Osvaldo Simeone
Bashir M. Al-Hashimi
86
4
0
27 Sep 2023
PAGER: A Framework for Failure Analysis of Deep Regression Models
PAGER: A Framework for Failure Analysis of Deep Regression Models
Jayaraman J. Thiagarajan
V. Narayanaswamy
Puja Trivedi
Rushil Anirudh
65
0
0
20 Sep 2023
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty
Information-theoretic Analysis of Test Data Sensitivity in Uncertainty
Futoshi Futami
Tomoharu Iwata
UDPER
62
3
0
23 Jul 2023
Efficient Uncertainty Quantification and Reduction for
  Over-Parameterized Neural Networks
Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks
Ziyi Huang
Henry Lam
Haofeng Zhang
UQCV
84
7
0
09 Jun 2023
Training-Free Neural Active Learning with Initialization-Robustness
  Guarantees
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
Apivich Hemachandra
Zhongxiang Dai
Jasraj Singh
See-Kiong Ng
K. H. Low
AAML
87
7
0
07 Jun 2023
Improving Neural Additive Models with Bayesian Principles
Improving Neural Additive Models with Bayesian Principles
Kouroche Bouchiat
Alexander Immer
Hugo Yèche
Gunnar Rätsch
Vincent Fortuin
BDLMedIm
105
6
0
26 May 2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
Agustinus Kristiadi
Alexander Immer
Runa Eschenhagen
Vincent Fortuin
BDLUQCV
80
10
0
17 Apr 2023
Bayesian Quadrature for Neural Ensemble Search
Bayesian Quadrature for Neural Ensemble Search
Saad Hamid
Xingchen Wan
Martin Jørgensen
Binxin Ru
Michael A. Osborne
BDLUQCV
69
1
0
15 Mar 2023
Toward Robust Uncertainty Estimation with Random Activation Functions
Toward Robust Uncertainty Estimation with Random Activation Functions
Y. Stoyanova
Soroush Ghandi
M. Tavakol
UQCV
70
2
0
28 Feb 2023
Width and Depth Limits Commute in Residual Networks
Width and Depth Limits Commute in Residual Networks
Soufiane Hayou
Greg Yang
90
14
0
01 Feb 2023
Estimating Causal Effects using a Multi-task Deep Ensemble
Estimating Causal Effects using a Multi-task Deep Ensemble
Ziyang Jiang
Zhuoran Hou
Yi-Ling Liu
Yiman Ren
Keyu Li
David Carlson
CML
76
6
0
26 Jan 2023
Bayesian Interpolation with Deep Linear Networks
Bayesian Interpolation with Deep Linear Networks
Boris Hanin
Alexander Zlokapa
151
26
0
29 Dec 2022
Improving Uncertainty Quantification of Variance Networks by
  Tree-Structured Learning
Improving Uncertainty Quantification of Variance Networks by Tree-Structured Learning
Wenxuan Ma
Xing Yan
Kun Zhang
UQCV
66
0
0
24 Dec 2022
Disentangling the Predictive Variance of Deep Ensembles through the
  Neural Tangent Kernel
Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel
Seijin Kobayashi
Pau Vilimelis Aceituno
J. Oswald
UQCV
87
3
0
18 Oct 2022
Learning Skills from Demonstrations: A Trend from Motion Primitives to
  Experience Abstraction
Learning Skills from Demonstrations: A Trend from Motion Primitives to Experience Abstraction
Mehrdad Tavassoli
S. Katyara
Maria Pozzi
Nikhil Deshpande
D. Caldwell
D. Prattichizzo
96
13
0
14 Oct 2022
Sample-Then-Optimize Batch Neural Thompson Sampling
Sample-Then-Optimize Batch Neural Thompson Sampling
Zhongxiang Dai
Yao Shu
Bryan Kian Hsiang Low
Patrick Jaillet
AAML
72
25
0
13 Oct 2022
On the infinite-depth limit of finite-width neural networks
On the infinite-depth limit of finite-width neural networks
Soufiane Hayou
97
23
0
03 Oct 2022
Scale-invariant Bayesian Neural Networks with Connectivity Tangent
  Kernel
Scale-invariant Bayesian Neural Networks with Connectivity Tangent Kernel
Sungyub Kim
Si-hun Park
Kyungsu Kim
Eunho Yang
BDL
86
5
0
30 Sep 2022
Single Model Uncertainty Estimation via Stochastic Data Centering
Single Model Uncertainty Estimation via Stochastic Data Centering
Jayaraman J. Thiagarajan
Rushil Anirudh
V. Narayanaswamy
P. Bremer
UQCVOOD
67
28
0
14 Jul 2022
A Fast, Well-Founded Approximation to the Empirical Neural Tangent
  Kernel
A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel
Mohamad Amin Mohamadi
Wonho Bae
Danica J. Sutherland
AAML
86
28
0
25 Jun 2022
Fast Finite Width Neural Tangent Kernel
Fast Finite Width Neural Tangent Kernel
Roman Novak
Jascha Narain Sohl-Dickstein
S. Schoenholz
AAML
65
56
0
17 Jun 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
96
20
0
08 Jun 2022
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