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1705.08933
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Doubly Stochastic Variational Inference for Deep Gaussian Processes
24 May 2017
Hugh Salimbeni
M. Deisenroth
BDL
GP
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Papers citing
"Doubly Stochastic Variational Inference for Deep Gaussian Processes"
50 / 238 papers shown
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Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks
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353
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Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial
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07 May 2023
Actually Sparse Variational Gaussian Processes
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Daniel Augusto R. M. A. de Souza
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Mark van der Wilk
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251
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11 Apr 2023
Calibrating Transformers via Sparse Gaussian Processes
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146
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Variational Linearized Laplace Approximation for Bayesian Deep Learning
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448
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Free-Form Variational Inference for Gaussian Process State-Space Models
International Conference on Machine Learning (ICML), 2023
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Edwin V. Bonilla
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235
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225
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Maurizio Filippone
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225
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Towards Flexibility and Interpretability of Gaussian Process State-Space Model
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Juan Maroñas
382
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Active Learning of Piecewise Gaussian Process Surrogates
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Soondo Hong
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303
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Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting
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236
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123
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44
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Sparse Gaussian Process Hyperparameters: Optimize or Integrate?
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V. Lalchand
W. Bruinsma
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173
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Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics
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Hany Abdulsamad
Peter Nickl
Pascal Klink
Jan Peters
194
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Joint control variate for faster black-box variational inference
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Xi Wang
Tomas Geffner
Justin Domke
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296
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13 Oct 2022
Computationally-efficient initialisation of GPs: The generalised variogram method
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Elsa Cazelles
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214
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Bézier Gaussian Processes for Tall and Wide Data
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Martin Jørgensen
Michael A. Osborne
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341
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01 Sep 2022
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition
International Conference on Machine Learning (ICML), 2022
Liang Luo
Shandian Zhe
74
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06 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection
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Konstantinos Kamnitsas
Ben Glocker
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281
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27 Jun 2022
Shallow and Deep Nonparametric Convolutions for Gaussian Processes
Thomas M. McDonald
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Mauricio A. Alvarez
143
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Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification
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101
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Luis A. Ortega
Simón Rodríguez Santana
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217
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Scalable Deep Gaussian Markov Random Fields for General Graphs
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Per Sidén
Fredrik Lindsten
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148
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10 Jun 2022
Multi-fidelity Hierarchical Neural Processes
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D. Wu
Matteo Chinazzi
Alessandro Vespignani
Yi-An Ma
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187
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Statistical Deep Learning for Spatial and Spatio-Temporal Data
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275
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Juan Maroñas
Daniel Hernández-Lobato
321
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30 May 2022
AK: Attentive Kernel for Information Gathering
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Roni Khardon
Lantao Liu
322
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Modelling calibration uncertainty in networks of environmental sensors
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Joel Ssematimba
Pablo A. Alvarado
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93
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A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
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J. Liu
Shreyas Padhy
Jie Jessie Ren
Zi Lin
Yeming Wen
Ghassen Jerfel
Zachary Nado
Jasper Snoek
Dustin Tran
Balaji Lakshminarayanan
UQCV
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487
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01 May 2022
A piece-wise constant approximation for non-conjugate Gaussian Process models
Sarem Seitz
84
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22 Apr 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
169
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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
324
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Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation
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Zehua Wang
Guogang Liao
Xiaowen Shi
Xiaoxu Wu
Wei Shen
Bingqin Zhu
Yongkang Wang
Xingxing Wang
Dong Wang
202
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Patrick Shafto
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335
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211
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216
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Triangulation candidates for Bayesian optimization
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305
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205
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Rui Tuo
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191
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219
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Ieva Kazlauskaite
Eky Febrianto
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291
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154
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