Doubly Stochastic Variational Inference for Deep Gaussian Processes

24 May 2017

Papers citing "Doubly Stochastic Variational Inference for Deep Gaussian Processes"

50 / 230 papers shown

Title
Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL UQCV 47 4 0 24 Feb 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Guided Deep Kernel Learning Idan Achituve Gal Chechik Ethan Fetaya BDL 31 5 0 19 Feb 2023
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow Victor Picheny Joel Berkeley Henry B. Moss Hrvoje Stojić Uri Granta ... Sergio Pascual-Diaz Stratis Markou Jixiang Qing Nasrulloh Loka Ivo Couckuyt 23 17 0 16 Feb 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes Ba-Hien Tran Babak Shahbaba Stephan Mandt Maurizio Filippone SyDa BDL UQCV 21 5 0 09 Feb 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 34 7 0 21 Jan 2023
Active Learning of Piecewise Gaussian Process Surrogates Chiwoo Park R. Waelder Bonggwon Kang Benji Maruyama Soondo Hong R. Gramacy GP 24 1 0 20 Jan 2023
A Pattern Discovery Approach to Multivariate Time Series Forecasting Yunyao Cheng Chenjuan Guo Kai Chen Kai Zhao B. Yang Jiandong Xie Christian S. Jensen Feiteng Huang Kai Zheng AI4TS 30 1 0 20 Dec 2022
A Flexible Nadaraya-Watson Head Can Offer Explainable and Calibrated Classification Alan Q. Wang M. Sabuncu 30 5 0 07 Dec 2022
Deep Gaussian Processes for Air Quality Inference Aadesh Desai Eshan Gujarathi Saagar Parikh Sachin Yadav Zeel B Patel Nipun Batra 36 2 0 18 Nov 2022
Ischemic Stroke Lesion Prediction using imbalanced Temporal Deep Gaussian Process (iTDGP) Mohsen Soltanpour Muhammad Yousefnezhad Russ Greiner Pierre Boulanger B. Buck 11 1 0 16 Nov 2022
Sparse Gaussian Process Hyperparameters: Optimize or Integrate? V. Lalchand W. Bruinsma David R. Burt C. Rasmussen GP 17 6 0 04 Nov 2022
Variational Hierarchical Mixtures for Probabilistic Learning of Inverse Dynamics Hany Abdulsamad Peter Nickl Pascal Klink Jan Peters 29 0 0 02 Nov 2022
Joint control variate for faster black-box variational inference Xi Wang Tomas Geffner Justin Domke BDL DRL 19 0 0 13 Oct 2022
Computationally-efficient initialisation of GPs: The generalised variogram method Felipe A. Tobar Elsa Cazelles T. Wolff 14 0 0 11 Oct 2022
Bézier Gaussian Processes for Tall and Wide Data Martin Jørgensen Michael A. Osborne GP 19 2 0 01 Sep 2022
Nonparametric Factor Trajectory Learning for Dynamic Tensor Decomposition Zihan Wang Shandian Zhe 11 5 0 06 Jul 2022
Distributional Gaussian Processes Layers for Out-of-Distribution Detection S. Popescu D. Sharp James H. Cole Konstantinos Kamnitsas Ben Glocker OOD 29 0 0 27 Jun 2022
Shallow and Deep Nonparametric Convolutions for Gaussian Processes Thomas M. McDonald M. Ross M. Smith Mauricio A. Alvarez 15 1 0 17 Jun 2022
Photoelectric Factor Prediction Using Automated Learning and Uncertainty Quantification K. Alsamadony A. Ibrahim S. Elkatatny A. Abdulraheem 17 1 0 17 Jun 2022
Deep Variational Implicit Processes Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL 23 5 0 14 Jun 2022
Scalable Deep Gaussian Markov Random Fields for General Graphs Joel Oskarsson Per Sidén Fredrik Lindsten BDL 11 3 0 10 Jun 2022
Multi-fidelity Hierarchical Neural Processes D. Wu Matteo Chinazzi Alessandro Vespignani Yi Ma Rose Yu AI4CE 16 13 0 10 Jun 2022
Statistical Deep Learning for Spatial and Spatio-Temporal Data C. Wikle A. Zammit‐Mangion BDL 21 45 0 05 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 19 6 0 30 May 2022
AK: Attentive Kernel for Information Gathering Weizhe (Wesley) Chen R. Khardon Lantao Liu 32 12 0 13 May 2022
Modelling calibration uncertainty in networks of environmental sensors M. Smith M. Ross Joel Ssematimba Pablo A. Alvarado Mauricio A. Alvarez Engineer Bainomugisha R. Wilkinson 6 3 0 04 May 2022
A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness J. Liu Shreyas Padhy Jie Jessie Ren Zi Lin Yeming Wen Ghassen Jerfel Zachary Nado Jasper Snoek Dustin Tran Balaji Lakshminarayanan UQCV BDL 26 48 0 01 May 2022
A piece-wise constant approximation for non-conjugate Gaussian Process models Sarem Seitz 14 0 0 22 Apr 2022
Gaussian Processes for Missing Value Imputation B. Jafrasteh Daniel Hernández-Lobato Simón Pedro Lubián López Isabel Benavente-Fernández GP 14 14 0 10 Apr 2022
Vecchia-approximated Deep Gaussian Processes for Computer Experiments Annie Sauer A. Cooper R. Gramacy 16 34 0 06 Apr 2022
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation Zehua Wang Guogang Liao Xiaowen Shi Xiaoxu Wu Chuheng Zhang Bingqin Zhu Yongkang Wang Xingxing Wang Dong Wang 17 4 0 02 Apr 2022
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes: Covariance, Expressivity, and Neural Tangent Kernel Chi-Ken Lu Patrick Shafto BDL 34 0 0 14 Mar 2022
Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference V. Lalchand Aditya Ravuri Neil D. Lawrence GP VLM 10 5 0 25 Feb 2022
Confident Neural Network Regression with Bootstrapped Deep Ensembles Laurens Sluijterman Eric Cator Tom Heskes BDL UQCV FedML 16 2 0 22 Feb 2022
Triangulation candidates for Bayesian optimization R. Gramacy Anna Sauer Nathan Wycoff 16 13 0 14 Dec 2021
Posterior contraction rates for constrained deep Gaussian processes in density estimation and classication F. Bachoc A. Lagnoux 26 4 0 14 Dec 2021
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 16 6 0 11 Dec 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 18 2 0 30 Oct 2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices Jan Povala Ieva Kazlauskaite Eky Febrianto F. Cirak Mark Girolami 32 22 0 22 Oct 2021
Bayesian Meta-Learning Through Variational Gaussian Processes Vivek Myers Nikhil Sardana BDL UQCV 4 4 0 21 Oct 2021
Nonnegative spatial factorization F. W. Townes Barbara E. Engelhardt 16 11 0 12 Oct 2021
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios Max Winkelmann Mike Kohlhoff H. Tadjine Steffen Müller 29 9 0 06 Oct 2021
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data Weichang Yu S. Wade H. Bondell Lamiae Azizi 17 3 0 29 Sep 2021
A theory of representation learning gives a deep generalisation of kernel methods Adam X. Yang Maxime Robeyns Edward Milsom Ben Anson Nandi Schoots Laurence Aitchison BDL 32 10 0 30 Aug 2021
A variational approximate posterior for the deep Wishart process Sebastian W. Ober Laurence Aitchison BDL 17 11 0 21 Jul 2021
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression Ayush Jain P. K. Srijith Mohammad Emtiyaz Khan BDL GP 16 0 0 17 Jul 2021
Input Dependent Sparse Gaussian Processes B. Jafrasteh Carlos Villacampa-Calvo Daniel Hernández-Lobato UQCV 20 5 0 15 Jul 2021
Deep Gaussian Process Emulation using Stochastic Imputation Deyu Ming D. Williamson S. Guillas 9 30 0 04 Jul 2021