GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration

28 September 2018

Papers citing "GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration"

50 / 207 papers shown

Title
Bayesian Optimization with High-Dimensional Outputs Wesley J. Maddox Maximilian Balandat A. Wilson E. Bakshy UQCV 24 50 0 24 Jun 2021
Ada-BKB: Scalable Gaussian Process Optimization on Continuous Domains by Adaptive Discretization Marco Rando Luigi Carratino S. Villa Lorenzo Rosasco 44 5 0 16 Jun 2021
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective Geoff Pleiss John P. Cunningham 28 24 0 11 Jun 2021
Meta-Learning Reliable Priors in the Function Space Jonas Rothfuss Dominique Heyn Jinfan Chen Andreas Krause 42 27 0 06 Jun 2021
Lookahead Acquisition Functions for Finite-Horizon Time-Dependent Bayesian Optimization and Application to Quantum Optimal Control Sudharshan Ashwin Renganathan Jeffrey Larson Stefan M. Wild 23 7 0 20 May 2021
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure Samuel Kim Peter Y. Lu Charlotte Loh Jamie Smith Jasper Snoek M. Soljavcić BDL AI4CE 175 17 0 23 Apr 2021
Fast Design Space Exploration of Nonlinear Systems: Part I S. Narain Emily Mak Dana Chee Brendan Englot K. Pochiraju N. Jha Karthik Narayan 25 5 0 05 Apr 2021
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB Sami Alabed Eiko Yoneki 21 17 0 30 Mar 2021
The Hintons in your Neural Network: a Quantum Field Theory View of Deep Learning Roberto Bondesan Max Welling 44 7 0 08 Mar 2021
Fast Adaptation with Linearized Neural Networks Wesley J. Maddox Shuai Tang Pablo G. Moreno A. Wilson Andreas C. Damianou 32 32 0 02 Mar 2021
Tighter Bounds on the Log Marginal Likelihood of Gaussian Process Regression Using Conjugate Gradients A. Artemev David R. Burt Mark van der Wilk 23 18 0 16 Feb 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski Luhuan Wu D. Biderman Geoff Pleiss John P. Cunningham 26 19 0 12 Feb 2021
A Tutorial on Sparse Gaussian Processes and Variational Inference Felix Leibfried Vincent Dutordoir S. T. John N. Durrande GP 42 49 0 27 Dec 2020
Amazon SageMaker Automatic Model Tuning: Scalable Gradient-Free Optimization Valerio Perrone Huibin Shen Aida Zolic I. Shcherbatyi Amr Ahmed ... Barbara Pogorzelska Miroslav Miladinovic K. Kenthapadi Matthias Seeger Cédric Archambeau 47 16 0 15 Dec 2020
Forecasting Emergency Department Capacity Constraints for COVID Isolation Beds Erik Drysdale Devin Singh Anna Goldenberg OOD 11 2 0 09 Nov 2020
Pathwise Conditioning of Gaussian Processes James T. Wilson Viacheslav Borovitskiy Alexander Terenin P. Mostowsky M. Deisenroth 18 58 0 08 Nov 2020
Transforming Gaussian Processes With Normalizing Flows Juan Maroñas Oliver Hamelijnck Jeremias Knoblauch Theodoros Damoulas 36 34 0 03 Nov 2020
Semi-parametric $γ$ -ray modeling with Gaussian processes and variational inference S. Mishra-Sharma Kyle Cranmer MedIm 21 7 0 20 Oct 2020
Asynchronous ε-Greedy Bayesian Optimisation George De Ath Richard Everson J. Fieldsend 35 5 0 15 Oct 2020
Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models E. Zelikman Sharon Zhou Jeremy Irvin Cooper D. Raterink Hao Sheng Anand Avati Jack Kelly Ram Rajagopal A. Ng D. Gagne 23 12 0 09 Oct 2020
Parametric Copula-GP model for analyzing multidimensional neuronal and behavioral relationships N. Kudryashova Theoklitos Amvrosiadis Nathalie Dupuy Nathalie L Rochefort A. Onken 19 5 0 03 Aug 2020
Convergence of Sparse Variational Inference in Gaussian Processes Regression David R. Burt C. Rasmussen Mark van der Wilk 29 69 0 01 Aug 2020
IntelligentPooling: Practical Thompson Sampling for mHealth Sabina Tomkins Peng Liao P. Klasnja Susan Murphy 39 30 0 31 Jul 2020
A Hierarchical Approach to Scaling Batch Active Search Over Structured Data Vivek Myers Peyton Greenside 36 1 0 20 Jul 2020
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees Shali Jiang Daniel R. Jiang Maximilian Balandat Brian Karrer Jacob R. Gardner Roman Garnett 21 44 0 29 Jun 2020
Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes Mengdi Xu Wenhao Ding Jiacheng Zhu Zuxin Liu Baiming Chen Ding Zhao CLL OffRL 30 34 0 19 Jun 2020
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization Geoff Pleiss M. Jankowiak David Eriksson Anil Damle Jacob R. Gardner 21 43 0 19 Jun 2020
Bayesian Optimization with Missing Inputs P. Luong Dang Nguyen Sunil R. Gupta Santu Rana Svetha Venkatesh 29 3 0 19 Jun 2020
Kernel methods through the roof: handling billions of points efficiently Giacomo Meanti Luigi Carratino Lorenzo Rosasco Alessandro Rudi 33 114 0 18 Jun 2020
Learning Rates as a Function of Batch Size: A Random Matrix Theory Approach to Neural Network Training Diego Granziol S. Zohren Stephen J. Roberts ODL 42 49 0 16 Jun 2020
Depth Uncertainty in Neural Networks Javier Antorán J. Allingham José Miguel Hernández-Lobato UQCV OOD BDL 46 100 0 15 Jun 2020
SAMBA: Safe Model-Based & Active Reinforcement Learning Alexander I. Cowen-Rivers Daniel Palenicek Vincent Moens Mohammed Abdullah Aivar Sootla Jun Wang Haitham Bou-Ammar 23 44 0 12 Jun 2020
Planning from Images with Deep Latent Gaussian Process Dynamics Nathanael Bosch Jan Achterhold Laura Leal-Taixé J. Stückler 25 1 0 07 May 2020
Multi-Sparse Gaussian Process: Learning based Semi-Parametric Control Mouhyemen Khan Akash Patel A. Chatterjee 4 2 0 03 Mar 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks Agustinus Kristiadi Matthias Hein Philipp Hennig BDL UQCV 38 277 0 24 Feb 2020
Rapidly Personalizing Mobile Health Treatment Policies with Limited Data Sabina Tomkins Peng Liao P. Klasnja Serena Yeung Susan Murphy 44 6 0 23 Feb 2020
Bayesian Deep Learning and a Probabilistic Perspective of Generalization A. Wilson Pavel Izmailov UQCV BDL OOD 24 642 0 20 Feb 2020
Scalable Constrained Bayesian Optimization David Eriksson Matthias Poloczek 33 95 0 20 Feb 2020
Residual Correlation in Graph Neural Network Regression Junteng Jia Austin R. Benson 33 25 0 19 Feb 2020
MOGPTK: The Multi-Output Gaussian Process Toolkit T. Wolff Alejandro Cuevas Felipe A. Tobar GP 22 45 0 09 Feb 2020
Randomly Projected Additive Gaussian Processes for Regression Ian A. Delbridge D. Bindel A. Wilson 27 27 0 30 Dec 2019
Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak Lechao Xiao Jiri Hron Jaehoon Lee Alexander A. Alemi Jascha Narain Sohl-Dickstein S. Schoenholz 38 225 0 05 Dec 2019
The Renyi Gaussian Process: Towards Improved Generalization Xubo Yue Raed Al Kontar 112 3 0 15 Oct 2019
BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization Maximilian Balandat Brian Karrer Daniel R. Jiang Sam Daulton Benjamin Letham A. Wilson E. Bakshy 32 93 0 14 Oct 2019
Scalable Global Optimization via Local Bayesian Optimization Samyam Rajbhandari Michael Pearce Jacob R. Gardner Ryan D. Turner Matthias Poloczek 51 452 0 03 Oct 2019
Subspace Inference for Bayesian Deep Learning Pavel Izmailov Wesley J. Maddox Polina Kirichenko T. Garipov Dmitry Vetrov A. Wilson UQCV BDL 38 143 0 17 Jul 2019
Neural Likelihoods for Multi-Output Gaussian Processes M. Jankowiak Jacob R. Gardner UQCV BDL 29 3 0 31 May 2019
Kernel Conditional Density Operators Ingmar Schuster Mattes Mollenhauer Stefan Klus Krikamol Muandet 30 25 0 27 May 2019
Constraining the Parameters of High-Dimensional Models with Active Learning S. Caron Tom Heskes Sydney Otten B. Stienen AI4CE 22 27 0 19 May 2019
Exact Gaussian Processes on a Million Data Points Ke Alexander Wang Geoff Pleiss Jacob R. Gardner Stephen Tyree Kilian Q. Weinberger A. Wilson GP 24 226 0 19 Mar 2019