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Differentiable Compositional Kernel Learning for Gaussian Processes

Differentiable Compositional Kernel Learning for Gaussian Processes

12 June 2018
Shengyang Sun
Guodong Zhang
Chaoqi Wang
Wenyuan Zeng
Jiaman Li
Roger C. Grosse
    BDL
ArXivPDFHTML

Papers citing "Differentiable Compositional Kernel Learning for Gaussian Processes"

13 / 13 papers shown
Title
Prediction-Oriented Bayesian Active Learning
Prediction-Oriented Bayesian Active Learning
Freddie Bickford-Smith
Andreas Kirsch
Sebastian Farquhar
Y. Gal
Adam Foster
Tom Rainforth
42
29
0
17 Apr 2023
Structural Kernel Search via Bayesian Optimization and Symbolical
  Optimal Transport
Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport
M. Bitzer
Mona Meister
Christoph Zimmer
12
9
0
21 Oct 2022
Modelling Non-Smooth Signals with Complex Spectral Structure
Modelling Non-Smooth Signals with Complex Spectral Structure
W. Bruinsma
Martin Tegnér
Richard Turner
30
6
0
14 Mar 2022
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning
Chi-Ken Lu
Patrick Shafto
BDL
27
4
0
01 Oct 2021
Online structural kernel selection for mobile health
Online structural kernel selection for mobile health
Eura Shin
Pedja Klasnja
S. Murphy
Finale Doshi-Velez
22
1
0
21 Jul 2021
Priors in Bayesian Deep Learning: A Review
Priors in Bayesian Deep Learning: A Review
Vincent Fortuin
UQCV
BDL
33
124
0
14 May 2021
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for
  Long-term Forecasting
Gaussian Processes with Skewed Laplace Spectral Mixture Kernels for Long-term Forecasting
Kai Chen
Twan van Laarhoven
E. Marchiori
AI4TS
39
8
0
08 Nov 2020
Maximum likelihood estimation and uncertainty quantification for
  Gaussian process approximation of deterministic functions
Maximum likelihood estimation and uncertainty quantification for Gaussian process approximation of deterministic functions
Toni Karvonen
George Wynne
Filip Tronarp
Chris J. Oates
Simo Särkkä
23
39
0
29 Jan 2020
Neural Likelihoods for Multi-Output Gaussian Processes
Neural Likelihoods for Multi-Output Gaussian Processes
M. Jankowiak
Jacob R. Gardner
UQCV
BDL
29
3
0
31 May 2019
Interpretable deep Gaussian processes with moments
Interpretable deep Gaussian processes with moments
Chi-Ken Lu
Scott Cheng-Hsin Yang
Xiaoran Hao
Patrick Shafto
23
19
0
27 May 2019
Functional Variational Bayesian Neural Networks
Functional Variational Bayesian Neural Networks
Shengyang Sun
Guodong Zhang
Jiaxin Shi
Roger C. Grosse
BDL
22
235
0
14 Mar 2019
Harmonizable mixture kernels with variational Fourier features
Harmonizable mixture kernels with variational Fourier features
Zheyan Shen
Markus Heinonen
Samuel Kaski
33
17
0
10 Oct 2018
Neural Processes
Neural Processes
M. Garnelo
Jonathan Richard Schwarz
Dan Rosenbaum
Fabio Viola
Danilo Jimenez Rezende
S. M. Ali Eslami
Yee Whye Teh
BDL
UQCV
GP
19
506
0
04 Jul 2018
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