Implicit Posterior Variational Inference for Deep Gaussian Processes

26 October 2019

Papers citing "Implicit Posterior Variational Inference for Deep Gaussian Processes"

26 / 26 papers shown

Title
Fully Bayesian Differential Gaussian Processes through Stochastic Differential Equations Jian Xu Zhiqi Lin Min Chen Junmei Yang Delu Zeng John Paisley 35 0 0 12 Aug 2024
Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference Jian Xu Delu Zeng John Paisley DiffM 39 1 0 24 Jul 2024
Machine Unlearning: A Comprehensive Survey Weiqi Wang Zhiyi Tian Chenhan Zhang Shui Yu MU AILaw 36 14 0 13 May 2024
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning Thomas Baldwin-McDonald Mauricio A. Álvarez 39 1 0 24 Nov 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 21 1 0 22 Sep 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
The Neural Process Family: Survey, Applications and Perspectives Saurav Jha Dong Gong Xuesong Wang Richard Turner L. Yao BDL 83 24 0 01 Sep 2022
Bayesian Optimization under Stochastic Delayed Feedback Arun Verma Zhongxiang Dai Bryan Kian Hsiang Low 17 12 0 19 Jun 2022
Deep Variational Implicit Processes Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL 28 5 0 14 Jun 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
Conditional Deep Gaussian Processes: empirical Bayes hyperdata learning Chi-Ken Lu Patrick Shafto BDL 27 4 0 01 Oct 2021
Subset-of-Data Variational Inference for Deep Gaussian-Processes Regression Ayush Jain P. K. Srijith Mohammad Emtiyaz Khan BDL GP 21 0 0 17 Jul 2021
Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features Thomas M. McDonald Mauricio A. Alvarez 19 10 0 10 Jun 2021
Convolutional Normalizing Flows for Deep Gaussian Processes Haibin Yu Dapeng Liu Yizhou Chen K. H. Low Patrick Jaillet BDL 31 6 0 17 Apr 2021
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges Moloud Abdar Farhad Pourpanah Sadiq Hussain Dana Rezazadegan Li Liu ... Xiaochun Cao Abbas Khosravi U. Acharya V. Makarenkov S. Nahavandi BDL UQCV 56 1,883 0 12 Nov 2020
Sample-efficient reinforcement learning using deep Gaussian processes Charles W. L. Gadd Markus Heinonen Harri Lähdesmäki Samuel Kaski GP BDL 31 4 0 02 Nov 2020
Variational Bayesian Unlearning Q. Nguyen Bryan Kian Hsiang Low Patrick Jaillet BDL MU 27 122 0 24 Oct 2020
Private Outsourced Bayesian Optimization D. Kharkovskii Zhongxiang Dai K. H. Low 20 21 0 24 Oct 2020
Federated Bayesian Optimization via Thompson Sampling Zhongxiang Dai K. H. Low Patrick Jaillet FedML 89 111 0 20 Oct 2020
R2-B2: Recursive Reasoning-Based Bayesian Optimization for No-Regret Learning in Games Zhongxiang Dai Yizhou Chen K. H. Low Patrick Jaillet Teck-Hua Ho 14 26 0 30 Jun 2020
Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties J. Lindinger David Reeb C. Lippert Barbara Rakitsch BDL UQCV 25 8 0 22 May 2020
Deep Latent-Variable Kernel Learning Haitao Liu Yew-Soon Ong Xiaomo Jiang Xiaofang Wang BDL 22 8 0 18 May 2020
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations Simone Rossi Markus Heinonen Edwin V. Bonilla Zheyan Shen Maurizio Filippone UQCV BDL 18 0 0 06 Mar 2020
Nonmyopic Gaussian Process Optimization with Macro-Actions D. Kharkovskii Chun Kai Ling K. H. Low 12 16 0 22 Feb 2020
Conditional Deep Gaussian Processes: multi-fidelity kernel learning Chi-Ken Lu Patrick Shafto 24 5 0 07 Feb 2020
Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression T. Teng Jie Chen Yehong Zhang K. H. Low BDL 14 23 0 05 Dec 2019