A Tutorial on Sparse Gaussian Processes and Variational Inference

27 December 2020

Papers citing "A Tutorial on Sparse Gaussian Processes and Variational Inference"

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Title
Gradient-based Sample Selection for Faster Bayesian Optimization Qiyu Wei Haowei Wang Zirui Cao Songhao Wang Richard Allmendinger Mauricio A Álvarez 40 0 0 10 Apr 2025
Data-Efficient Kernel Methods for Learning Differential Equations and Their Solution Operators: Algorithms and Error Analysis Yasamin Jalalian Juan Felipe Osorio Ramirez Alexander W. Hsu Bamdad Hosseini H. Owhadi 115 0 0 02 Mar 2025
Specialization in Hierarchical Learning Systems Heinke Hihn Daniel A. Braun 55 16 0 03 Nov 2020
Sparse Gaussian Processes with Spherical Harmonic Features Vincent Dutordoir N. Durrande J. Hensman 37 54 0 30 Jun 2020
Variational Orthogonal Features David R. Burt C. Rasmussen Mark van der Wilk BDL DRL 14 12 0 23 Jun 2020
Matérn Gaussian processes on Riemannian manifolds Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 33 122 0 17 Jun 2020
Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming Gabriel Riutort-Mayol Paul-Christian Bürkner Michael R. Andersen Arno Solin Aki Vehtari 39 70 0 23 Apr 2020
A Framework for Interdomain and Multioutput Gaussian Processes Mark van der Wilk Vincent Dutordoir S. T. John A. Artemev Vincent Adam J. Hensman 50 94 0 02 Mar 2020
Efficiently Sampling Functions from Gaussian Process Posteriors James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 16 163 0 21 Feb 2020
Decision-Making with Auto-Encoding Variational Bayes Romain Lopez Pierre Boyeau Nir Yosef Michael I. Jordan Jeffrey Regier BDL 109 10,591 0 17 Feb 2020
Doubly Sparse Variational Gaussian Processes Vincent Adam Stefanos Eleftheriadis N. Durrande A. Artemev J. Hensman 32 25 0 15 Jan 2020
Mutual-Information Regularization in Markov Decision Processes and Actor-Critic Learning Felix Leibfried Jordi Grau-Moya 29 22 0 11 Sep 2019
A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment Felix Leibfried Sergio Pascual-Diaz Jordi Grau-Moya 73 28 0 26 Jul 2019
An Information-theoretic On-line Learning Principle for Specialization in Hierarchical Decision-Making Systems Heinke Hihn Sebastian Gottwald Daniel A. Braun 35 16 0 26 Jul 2019
Benchmarking Model-Based Reinforcement Learning Tingwu Wang Xuchan Bao I. Clavera Jerrick Hoang Yeming Wen Eric D. Langlois Matthew Shunshi Zhang Guodong Zhang Pieter Abbeel Jimmy Ba OffRL 44 361 0 03 Jul 2019
Disentangled Skill Embeddings for Reinforcement Learning Janith C. Petangoda Sergio Pascual-Diaz Vincent Adam Peter Vrancx Jordi Grau-Moya DRL OffRL 31 15 0 21 Jun 2019
Exploring Model-based Planning with Policy Networks Tingwu Wang Jimmy Ba 54 148 0 20 Jun 2019
When to Trust Your Model: Model-Based Policy Optimization Michael Janner Justin Fu Marvin Zhang Sergey Levine OffRL 46 939 0 19 Jun 2019
Deep Gaussian Processes with Importance-Weighted Variational Inference Hugh Salimbeni Vincent Dutordoir J. Hensman M. Deisenroth BDL 52 43 0 14 May 2019
Rates of Convergence for Sparse Variational Gaussian Process Regression David R. Burt C. Rasmussen Mark van der Wilk 40 152 0 08 Mar 2019
Model-Based Reinforcement Learning for Atari Lukasz Kaiser Mohammad Babaeizadeh Piotr Milos B. Osinski R. Campbell ... Sergey Levine Afroz Mohiuddin Ryan Sepassi George Tucker Henryk Michalewski OffRL 84 851 0 01 Mar 2019
Bayesian Image Classification with Deep Convolutional Gaussian Processes Vincent Dutordoir Mark van der Wilk A. Artemev J. Hensman UQCV BDL 107 32 0 15 Feb 2019
Soft Actor-Critic Algorithms and Applications Tuomas Haarnoja Aurick Zhou Kristian Hartikainen George Tucker Sehoon Ha ... Vikash Kumar Henry Zhu Abhishek Gupta Pieter Abbeel Sergey Levine 94 2,391 0 13 Dec 2018
Bayesian Layers: A Module for Neural Network Uncertainty Dustin Tran Michael W. Dusenberry Mark van der Wilk Danijar Hafner UQCV BDL 84 121 0 10 Dec 2018
Gaussian Process Conditional Density Estimation Vincent Dutordoir Hugh Salimbeni M. Deisenroth J. Hensman 51 52 0 30 Oct 2018
Deep convolutional Gaussian processes Kenneth Blomqvist Samuel Kaski Markus Heinonen BDL 48 60 0 06 Oct 2018
GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Jacob R. Gardner Geoff Pleiss D. Bindel Kilian Q. Weinberger A. Wilson GP 56 1,088 0 28 Sep 2018
Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks Felix Leibfried Peter Vrancx OffRL 31 7 0 06 Sep 2018
Importance Weighting and Variational Inference Justin Domke Daniel Sheldon 34 107 0 27 Aug 2018
Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees Yuping Luo Huazhe Xu Yuanzhi Li Yuandong Tian Trevor Darrell Tengyu Ma OffRL 87 225 0 10 Jul 2018
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models Kurtland Chua Roberto Calandra R. McAllister Sergey Levine BDL 111 1,263 0 30 May 2018
Variational Inference for Data-Efficient Model Learning in POMDPs Sebastian Tschiatschek Kai Arulkumaran Jan Stühmer Katja Hofmann 31 15 0 23 May 2018
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review Sergey Levine AI4CE BDL 41 667 0 02 May 2018
An information-theoretic on-line update principle for perception-action coupling Zhen Peng Tim Genewein Felix Leibfried Daniel A. Braun 24 13 0 16 Apr 2018
Model-Ensemble Trust-Region Policy Optimization Thanard Kurutach I. Clavera Yan Duan Aviv Tamar Pieter Abbeel 33 450 0 28 Feb 2018
Balancing Two-Player Stochastic Games with Soft Q-Learning Jordi Grau-Moya Felix Leibfried Haitham Bou-Ammar 74 42 0 09 Feb 2018
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor Tuomas Haarnoja Aurick Zhou Pieter Abbeel Sergey Levine 159 8,236 0 04 Jan 2018
Convolutional Gaussian Processes Mark van der Wilk C. Rasmussen J. Hensman BDL 45 130 0 06 Sep 2017
Neural Network Dynamics for Model-Based Deep Reinforcement Learning with Model-Free Fine-Tuning Anusha Nagabandi G. Kahn R. Fearing Sergey Levine 62 967 0 08 Aug 2017
An Information-Theoretic Optimality Principle for Deep Reinforcement Learning Felix Leibfried Jordi Grau-Moya Haitham Bou-Ammar 69 24 0 06 Aug 2017
Data-Efficient Reinforcement Learning with Probabilistic Model Predictive Control Sanket Kamthe M. Deisenroth 91 217 0 20 Jun 2017
Identification of Gaussian Process State Space Models Stefanos Eleftheriadis Tom Nicholson M. Deisenroth J. Hensman 52 111 0 30 May 2017
Doubly Stochastic Variational Inference for Deep Gaussian Processes Hugh Salimbeni M. Deisenroth BDL GP 61 418 0 24 May 2017
A unified view of entropy-regularized Markov decision processes Gergely Neu Anders Jonsson Vicencc Gómez 76 255 0 22 May 2017
Equivalence Between Policy Gradients and Soft Q-Learning John Schulman Xi Chen Pieter Abbeel OffRL 52 344 0 21 Apr 2017
Bridging the Gap Between Value and Policy Based Reinforcement Learning Ofir Nachum Mohammad Norouzi Kelvin Xu Dale Schuurmans 72 469 0 28 Feb 2017
Reinforcement Learning with Deep Energy-Based Policies Tuomas Haarnoja Haoran Tang Pieter Abbeel Sergey Levine 42 1,329 0 27 Feb 2017
Variational Fourier features for Gaussian processes J. Hensman N. Durrande Arno Solin VLM 39 200 0 21 Nov 2016
GPflow: A Gaussian process library using TensorFlow A. G. Matthews Mark van der Wilk T. Nickson Keisuke Fujii A. Boukouvalas Pablo León-Villagrá Zoubin Ghahramani J. Hensman GP 53 662 0 27 Oct 2016
Chained Gaussian Processes Alan D. Saul J. Hensman Aki Vehtari Neil D. Lawrence 22 59 0 18 Apr 2016