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1602.04133
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Deep Gaussian Processes for Regression using Approximate Expectation Propagation
12 February 2016
T. Bui
Daniel Hernández-Lobato
Yingzhen Li
José Miguel Hernández-Lobato
Richard Turner
BDL
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Papers citing
"Deep Gaussian Processes for Regression using Approximate Expectation Propagation"
50 / 96 papers shown
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Jongjin Lee
Yunseop Shin
Ilsang Ohn
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0
22 Oct 2025
SDG-L: A Semiparametric Deep Gaussian Process based Framework for Battery Capacity Prediction
IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2023
Hanbing Liu
Y. Wu
Yang Li
Ercan E. Kuruoglu
Xuan Zhang
BDL
217
1
0
12 Oct 2025
Unlearning in Diffusion models under Data Constraints: A Variational Inference Approach
Subhodip Panda
MS Varun
Shreyans Jain
Sarthak Kumar Maharana
Prathosh A.P.
MU
355
0
0
05 Oct 2025
Gaussian Process Kolmogorov-Arnold Networks
Andrew Siyuan Chen
301
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25 Jul 2024
Fearless Stochasticity in Expectation Propagation
Jonathan So
Richard Turner
265
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0
03 Jun 2024
Variational Continual Test-Time Adaptation
Fan Lyu
Kaile Du
Yuyang Li
Hanyu Zhao
Zhang Zhang
Guangcan Liu
Guangcan Liu
Liang Wang
TTA
BDL
577
6
0
13 Feb 2024
Collapsed Inference for Bayesian Deep Learning
Neural Information Processing Systems (NeurIPS), 2023
Zhe Zeng
Karen Ullrich
FedML
BDL
UQCV
389
10
0
16 Jun 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
Mechanical systems and signal processing (MSSP), 2023
V. Nemani
Luca Biggio
Xun Huan
Zhen Hu
Olga Fink
Anh Tran
Yan Wang
Xiaoge Zhang
Chao Hu
AI4CE
324
151
0
07 May 2023
Deep networks for system identification: a Survey
G. Pillonetto
Aleksandr Aravkin
Daniel Gedon
L. Ljung
Antônio H. Ribeiro
Thomas B. Schon
OOD
372
104
0
30 Jan 2023
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification
Alan Q. Wang
M. Sabuncu
308
6
0
07 Dec 2022
An Anomaly Detection Method for Satellites Using Monte Carlo Dropout
IEEE Transactions on Aerospace and Electronic Systems (TAES), 2022
Mohammad Amin Maleki Sadr
Yeying Zhu
Peng Hu
BDL
186
26
0
27 Nov 2022
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
Thomas M. McDonald
M. Ross
M. Smith
Mauricio A. Alvarez
208
1
0
17 Jun 2022
Deep Variational Implicit Processes
International Conference on Learning Representations (ICLR), 2022
Luis A. Ortega
Simón Rodríguez Santana
Daniel Hernández-Lobato
BDL
298
6
0
14 Jun 2022
DNNR: Differential Nearest Neighbors Regression
International Conference on Machine Learning (ICML), 2022
Youssef Nader
Leon Sixt
Tim Landgraf
667
20
0
17 May 2022
Deep neural networks with dependent weights: Gaussian Process mixture limit, heavy tails, sparsity and compressibility
Journal of machine learning research (JMLR), 2022
Hoileong Lee
Fadhel Ayed
Paul Jung
Juho Lee
Hongseok Yang
François Caron
264
16
0
17 May 2022
Gaussian Processes for Missing Value Imputation
Knowledge-Based Systems (KBS), 2022
B. Jafrasteh
Daniel Hernández-Lobato
Simón Pedro Lubián López
Isabel Benavente-Fernández
GP
216
29
0
10 Apr 2022
Vecchia-approximated Deep Gaussian Processes for Computer Experiments
Journal of Computational And Graphical Statistics (JCGS), 2022
Annie Sauer
A. Cooper
R. Gramacy
371
51
0
06 Apr 2022
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel
Chi-Ken Lu
Patrick Shafto
BDL
396
1
0
14 Mar 2022
Modelling Non-Smooth Signals with Complex Spectral Structure
International Conference on Artificial Intelligence and Statistics (AISTATS), 2022
W. Bruinsma
Martin Tegnér
Richard Turner
292
6
0
14 Mar 2022
A Sparse Expansion For Deep Gaussian Processes
Liang Ding
Rui Tuo
Shahin Shahrampour
234
9
0
11 Dec 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Chi-Ken Lu
Patrick Shafto
BDL
336
5
0
01 Oct 2021
Measuring Uncertainty in Signal Fingerprinting with Gaussian Processes Going Deep
International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2021
R. Guan
Andi Zhang
Mengchao Li
Yongliang Wang
159
9
0
01 Sep 2021
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression
Conference on Uncertainty in Artificial Intelligence (UAI), 2021
Ayush Jain
P. K. Srijith
Mohammad Emtiyaz Khan
BDL
GP
219
0
0
17 Jul 2021
Deep Gaussian Process Emulation using Stochastic Imputation
Deyu Ming
D. Williamson
S. Guillas
296
39
0
04 Jul 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
Neural Information Processing Systems (NeurIPS), 2021
Geoff Pleiss
John P. Cunningham
335
30
0
11 Jun 2021
Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning
Neural Information Processing Systems (NeurIPS), 2021
Jannik Kossen
Neil Band
Clare Lyle
Aidan Gomez
Tom Rainforth
Y. Gal
OOD
3DPC
368
166
0
04 Jun 2021
Priors in Bayesian Deep Learning: A Review
International Statistical Review (ISR), 2021
Vincent Fortuin
UQCV
BDL
568
169
0
14 May 2021
Deep Neural Networks as Point Estimates for Deep Gaussian Processes
Neural Information Processing Systems (NeurIPS), 2021
Vincent Dutordoir
J. Hensman
Mark van der Wilk
Carl Henrik Ek
Zoubin Ghahramani
N. Durrande
BDL
UQCV
353
33
0
10 May 2021
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
Sebastian Houben
Stephanie Abrecht
Maram Akila
Andreas Bär
Felix Brockherde
...
Serin Varghese
Michael Weber
Sebastian J. Wirkert
Tim Wirtz
Matthias Woehrle
AAML
366
61
0
29 Apr 2021
Convolutional Normalizing Flows for Deep Gaussian Processes
IEEE International Joint Conference on Neural Network (IJCNN), 2021
Haibin Yu
Dapeng Liu
Yizhou Chen
K. H. Low
Patrick Jaillet
BDL
261
6
0
17 Apr 2021
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
Heng Hao
H. Moon
Sima Didari
J. Woo
P. Bangert
AI4TS
360
0
0
25 Feb 2021
Recent advances in deep learning theory
Fengxiang He
Dacheng Tao
AI4CE
402
57
0
20 Dec 2020
Active Learning for Deep Gaussian Process Surrogates
Annie Sauer
R. Gramacy
D. Higdon
GP
AI4CE
433
130
0
15 Dec 2020
On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes
International Conference on Machine Learning (ICML), 2020
Tim G. J. Rudner
Oscar Key
Y. Gal
Tom Rainforth
250
4
0
01 Nov 2020
Inter-domain Deep Gaussian Processes
International Conference on Machine Learning (ICML), 2020
Tim G. J. Rudner
Dino Sejdinovic
Yarin Gal
353
13
0
01 Nov 2020
Hierarchical Gaussian Processes with Wasserstein-2 Kernels
S. Popescu
D. Sharp
James H. Cole
Ben Glocker
363
5
0
28 Oct 2020
Characterizing Deep Gaussian Processes via Nonlinear Recurrence Systems
AAAI Conference on Artificial Intelligence (AAAI), 2020
Anh Tong
Jaesik Choi
444
2
0
19 Oct 2020
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Neural Information Processing Systems (NeurIPS), 2020
A. Tompkins
Rafael Oliveira
F. Ramos
259
8
0
09 Oct 2020
Epoch-evolving Gaussian Process Guided Learning
Jiabao Cui
Xuewei Li
Bin Li
Hanbin Zhao
Bourahla Omar
Xi Li
BDL
182
0
0
25 Jun 2020
The Statistical Cost of Robust Kernel Hyperparameter Tuning
Neural Information Processing Systems (NeurIPS), 2020
R. A. Meyer
Christopher Musco
225
3
0
14 Jun 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties
J. Lindinger
David Reeb
Christoph Lippert
Barbara Rakitsch
BDL
UQCV
265
8
0
22 May 2020
Energy-Based Processes for Exchangeable Data
International Conference on Machine Learning (ICML), 2020
Mengjiao Yang
Bo Dai
H. Dai
Dale Schuurmans
274
13
0
17 Mar 2020
Avoiding Kernel Fixed Points: Computing with ELU and GELU Infinite Networks
AAAI Conference on Artificial Intelligence (AAAI), 2020
Russell Tsuchida
Tim Pearce
Christopher van der Heide
Fred Roosta
M. Gallagher
394
10
0
20 Feb 2020
Transport Gaussian Processes for Regression
Gonzalo Rios
GP
235
6
0
30 Jan 2020
Hydra: Preserving Ensemble Diversity for Model Distillation
Linh-Tam Tran
Bastiaan S. Veeling
Kevin Roth
J. Swiatkowski
Joshua V. Dillon
Jasper Snoek
Stephan Mandt
Tim Salimans
Sebastian Nowozin
Rodolphe Jenatton
272
65
0
14 Jan 2020
A Systematic Comparison of Bayesian Deep Learning Robustness in Diabetic Retinopathy Tasks
Angelos Filos
Sebastian Farquhar
Aidan Gomez
Tim G. J. Rudner
Zachary Kenton
Lewis Smith
Milad Alizadeh
A. D. Kroon
Y. Gal
BDL
AAML
OOD
UQCV
372
123
0
22 Dec 2019
Benchmarking the Neural Linear Model for Regression
Sebastian W. Ober
C. Rasmussen
BDL
235
46
0
18 Dec 2019
Gaussian Process Priors for View-Aware Inference
AAAI Conference on Artificial Intelligence (AAAI), 2019
Wenshuai Zhao
Ari Heljakka
Arno Solin
BDL
219
1
0
06 Dec 2019
Warped Input Gaussian Processes for Time Series Forecasting
International Conference on Cyber Security Cryptography and Machine Learning (ICCSCML), 2019
David Tolpin
AI4TS
215
3
0
05 Dec 2019
Implicit Posterior Variational Inference for Deep Gaussian Processes
Neural Information Processing Systems (NeurIPS), 2019
Haibin Yu
Yizhou Chen
Zhongxiang Dai
K. H. Low
Patrick Jaillet
339
44
0
26 Oct 2019
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