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
Integrative Analysis and Imputation of Multiple Data Streams via Deep Gaussian Processes Ali Akbar Septiandri Deyu Ming F. Alejandro DiazDelaO Takoua Jendoubi Samiran Ray 2 0 0 17 May 2025
Evaluating Uncertainty in Deep Gaussian Processes Matthijs van der Lende Jeremias Lino Ferrao Niclas Müller-Hof UQCV 35 0 0 24 Apr 2025
Stochastic Process Learning via Operator Flow Matching Yaozhong Shi Zachary E. Ross D. Asimaki Kamyar Azizzadenesheli 52 1 0 10 Jan 2025
Residual Deep Gaussian Processes on Manifolds Kacper Wyrwal Andreas Krause Viacheslav Borovitskiy BDL 49 0 0 31 Oct 2024
Deep Q-Exponential Processes Zhi Chang Chukwudi Obite Shuang Zhou Shiwei Lan BDL 28 0 0 29 Oct 2024
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation Koshi Watanabe Keisuke Maeda Takahiro Ogawa Miki Haseyama 135 0 0 22 Oct 2024
Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines Edward Milsom Ben Anson Laurence Aitchison 28 0 0 08 Oct 2024
Physics-Informed Variational State-Space Gaussian Processes Oliver Hamelijnck Arno Solin Theodoros Damoulas 31 0 0 20 Sep 2024
Amortized Variational Inference for Deep Gaussian Processes Qiuxian Meng Yongyou Zhang 28 0 0 18 Sep 2024
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling Jian Xu Shian Du Junmei Yang Qianli Ma Delu Zeng BDL 31 0 0 13 Aug 2024
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations Jian Xu Zhiqi Lin Min Chen Junmei Yang Delu Zeng John Paisley 32 0 0 12 Aug 2024
Flexible Bayesian Last Layer Models Using Implicit Priors and Diffusion Posterior Sampling Jian Xu Zhiqi Lin Shigui Li Min Chen Junmei Yang Delu Zeng John Paisley BDL 28 0 0 07 Aug 2024
Monotonic warpings for additive and deep Gaussian processes Chang Liu Lauren J. Beesley Annie S. Booth Robert B. Gramacy Yan Zhao 27 2 0 02 Aug 2024
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference Jian Xu Delu Zeng John Paisley DiffM 31 1 0 24 Jul 2024
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference Xiaoyu Jiang Sokratia Georgaka Magnus Rattray Mauricio A. Alvarez 31 0 0 02 Jul 2024
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Xinxing Shi Thomas Baldwin-McDonald Mauricio A. Álvarez 76 0 0 01 Jul 2024
Latent Variable Double Gaussian Process Model for Decoding Complex Neural Data Navid Ziaei Joshua J. Stim Melanie D. Goodman-Keiser S. Sponheim A. Widge Sasoun Krikorian Ali Yousefi 20 0 0 08 May 2024
Multi-fidelity Gaussian process surrogate modeling for regression problems in physics Kislaya Ravi Vladyslav Fediukov Felix Dietrich T. Neckel Fabian Buse Michael Bergmann H. Bungartz AI4CE 39 6 0 18 Apr 2024
Universal Functional Regression with Neural Operator Flows Yaozhong Shi Angela F. Gao Zachary E. Ross Kamyar Azizzadenesheli 37 3 0 03 Apr 2024
On Uncertainty Quantification for Near-Bayes Optimal Algorithms Ziyu Wang Chris Holmes UQCV 42 2 0 28 Mar 2024
Deep Horseshoe Gaussian Processes Ismael Castillo Thibault Randrianarisoa BDL UQCV 42 5 0 04 Mar 2024
Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling Ruijia Niu D. Wu Kai Kim Yi Ma D. Watson‐Parris Rose Yu AI4CE 37 2 0 29 Feb 2024
Stopping Bayesian Optimization with Probabilistic Regret Bounds James T. Wilson 41 4 0 26 Feb 2024
Gradient-enhanced deep Gaussian processes for multifidelity modelling Viv Bone Chris van der Heide Kieran Mackle Ingo Jahn P. Dower Chris Manzie 25 1 0 25 Feb 2024
Multi-Fidelity Methods for Optimization: A Survey Ke Li Fan Li AI4CE 35 6 0 15 Feb 2024
Flexible infinite-width graph convolutional networks and the importance of representation learning Ben Anson Edward Milsom Laurence Aitchison SSL GNN 32 1 0 09 Feb 2024
Self-Attention through Kernel-Eigen Pair Sparse Variational Gaussian Processes Yingyi Chen Qinghua Tao F. Tonin Johan A. K. Suykens 22 1 0 02 Feb 2024
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data Navid Ziaei Behzad Nazari Uri T. Eden A. Widge Ali Yousefi 20 3 0 29 Jan 2024
Sparse Variational Student-t Processes Jian Xu Delu Zeng 26 1 0 09 Dec 2023
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning Thomas Baldwin-McDonald Mauricio A. Álvarez 39 1 0 24 Nov 2023
Deep Transformed Gaussian Processes Francisco Javier Sáez-Maldonado Juan Maroñas Daniel Hernández-Lobato 15 0 0 27 Oct 2023
Thin and Deep Gaussian Processes Daniel Augusto R. M. A. de Souza Alexander Nikitin S. T. John Magnus Ross Mauricio A. Alvarez M. Deisenroth Joao P. P. Gomes Diego Mesquita C. L. C. Mattos 22 5 0 17 Oct 2023
On permutation symmetries in Bayesian neural network posteriors: a variational perspective Simone Rossi Ankit Singh T. Hannagan 32 2 0 16 Oct 2023
Safe Exploration in Reinforcement Learning: A Generalized Formulation and Algorithms Akifumi Wachi Wataru Hashimoto Xun Shen Kazumune Hashimoto 22 9 0 05 Oct 2023
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes Jian Xu Shian Du Junmei Yang Qianli Ma Delu Zeng BDL 16 1 0 22 Sep 2023
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes M. Noack Hengrui Luo M. Risser GP 32 11 0 18 Sep 2023
Sampling-Free Probabilistic Deep State-Space Models Andreas Look M. Kandemir Barbara Rakitsch Jan Peters 16 2 0 15 Sep 2023
To Predict or to Reject: Causal Effect Estimation with Uncertainty on Networked Data Hechuan Wen Tong Chen Li Kheng Chai S. Sadiq Kai Zheng Hongzhi Yin CML 24 1 0 15 Sep 2023
Out of Distribution Detection via Domain-Informed Gaussian Process State Space Models Alonso Marco Elias Morley Claire Tomlin 34 2 0 13 Sep 2023
On the meaning of uncertainty for ethical AI: philosophy and practice Cassandra Bird Daniel Williamson Sabina Leonelli 6 1 0 11 Sep 2023
Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study A. Hazra Pratik Nag Rishikesh Yadav Ying Sun 23 3 0 10 Aug 2023
A comparison of machine learning surrogate models of street-scale flooding in Norfolk, Virginia Diana McSpadden S. Goldenberg Bina Roy M. Schram J. Goodall H. Lipford AI4CE 36 3 0 26 Jul 2023
Adaptive Robotic Information Gathering via Non-Stationary Gaussian Processes Weizhe (Wesley) Chen R. Khardon Lantao Liu 29 9 0 02 Jun 2023
Linked Deep Gaussian Process Emulation for Model Networks Deyu Ming D. Williamson 13 0 0 02 Jun 2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez Matthias Katzfuss BDL UQCV 64 1 0 26 May 2023
Distribution-Free Model-Agnostic Regression Calibration via Nonparametric Methods Shang Liu Zhongze Cai Xiaocheng Li 24 4 0 20 May 2023
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 33 75 0 07 May 2023
Actually Sparse Variational Gaussian Processes Harry Jake Cunningham Daniel Augusto R. M. A. de Souza So Takao Mark van der Wilk M. Deisenroth 32 5 0 11 Apr 2023
Calibrating Transformers via Sparse Gaussian Processes Wenlong Chen Yingzhen Li UQCV 32 12 0 04 Mar 2023
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes M. Ross Markus Heinonen 18 2 0 03 Mar 2023