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
Transformer Neural Processes: Uncertainty-Aware Meta Learning Via Sequence Modeling Tung Nguyen Aditya Grover BDL UQCV 29 99 0 09 Jul 2022
Parametric and Multivariate Uncertainty Calibration for Regression and Object Detection Fabian Küppers Jonas Schneider Anselm Haselhoff UQCV 39 8 0 04 Jul 2022
Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces Alexander Thebelt Calvin Tsay Robert M. Lee Nathan Sudermann-Merx David Walz B. Shafei Ruth Misener UQCV BDL 50 10 0 02 Jul 2022
Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels Mohamad Amin Mohamadi Wonho Bae Danica J. Sutherland 35 20 0 25 Jun 2022
Joint Entropy Search for Maximally-Informed Bayesian Optimization Carl Hvarfner Frank Hutter Luigi Nardi 46 36 0 09 Jun 2022
Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination Masaki Adachi Satoshi Hayakawa Martin Jørgensen Harald Oberhauser Michael A. Osborne 31 20 0 09 Jun 2022
Integrating Random Effects in Deep Neural Networks Giora Simchoni Saharon Rosset BDL AI4CE 40 21 0 07 Jun 2022
Posterior and Computational Uncertainty in Gaussian Processes Jonathan Wenger Geoff Pleiss Marvin Pfortner Philipp Hennig John P. Cunningham 83 19 0 30 May 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 27 6 0 30 May 2022
Fast Gaussian Process Posterior Mean Prediction via Local Cross Validation and Precomputation Alec M. Dunton Benjamin W. Priest Amanda Muyskens GP 32 3 0 22 May 2022
Bayesian Active Learning with Fully Bayesian Gaussian Processes Christoffer Riis Francisco Antunes F. B. Hüttel C. L. Azevedo Francisco Câmara Pereira GP 33 22 0 20 May 2022
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution Lixue Cheng Ziyi Yang Chang-Yu Hsieh Ben Liao Shengyu Zhang 32 6 0 19 May 2022
Universal characteristics of deep neural network loss surfaces from random matrix theory Nicholas P. Baskerville J. Keating F. Mezzadri J. Najnudel Diego Granziol 34 4 0 17 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 32 4 0 15 May 2022
Efficient Learning of Inverse Dynamics Models for Adaptive Computed Torque Control David Jorge Gabriella Pizzuto M. Mistry 22 3 0 10 May 2022
Machine Learning Diffusion Monte Carlo Energies Kevin Ryczko J. Krogel Isaac Tamblyn DiffM 16 14 0 09 May 2022
Accelerating Bayesian Optimization for Biological Sequence Design with Denoising Autoencoders Samuel Stanton Wesley J. Maddox Nate Gruver Phillip M. Maffettone E. Delaney Peyton Greenside A. Wilson BDL 43 90 0 23 Mar 2022
A novel sampler for Gauss-Hermite determinantal point processes with application to Monte Carlo integration Nicholas P. Baskerville 35 0 0 15 Mar 2022
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes Felix Jimenez Matthias Katzfuss 26 10 0 02 Mar 2022
Uncertainty Estimation for Computed Tomography with a Linearised Deep Image Prior Javier Antorán Riccardo Barbano Johannes Leuschner José Miguel Hernández-Lobato Bangti Jin UQCV 48 10 0 28 Feb 2022
Parallel MCMC Without Embarrassing Failures Daniel Augusto R. M. A. de Souza Diego Mesquita Samuel Kaski Luigi Acerbi 49 11 0 22 Feb 2022
Deep Ensembles Work, But Are They Necessary? Taiga Abe E. Kelly Buchanan Geoff Pleiss R. Zemel John P. Cunningham OOD UQCV 44 60 0 14 Feb 2022
Incorporating Sum Constraints into Multitask Gaussian Processes Philipp Pilar Carl Jidling Thomas B. Schon Niklas Wahlström TPM 24 3 0 03 Feb 2022
Posterior temperature optimized Bayesian models for inverse problems in medical imaging M. Laves Malte Tolle Alexander Schlaefer Sandy Engelhardt 48 10 0 02 Feb 2022
Giga-scale Kernel Matrix Vector Multiplication on GPU Robert Hu Siu Lun Chau Dino Sejdinovic J. Glaunès 29 2 0 02 Feb 2022
Local Latent Space Bayesian Optimization over Structured Inputs Natalie Maus Haydn Thomas Jones Juston Moore Matt J. Kusner John Bradshaw Jacob R. Gardner BDL 61 70 0 28 Jan 2022
Efficient Hyperparameter Tuning for Large Scale Kernel Ridge Regression Giacomo Meanti Luigi Carratino Ernesto De Vito Lorenzo Rosasco 24 12 0 17 Jan 2022
Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next S. Cuomo Vincenzo Schiano Di Cola F. Giampaolo G. Rozza Maizar Raissi F. Piccialli PINN 33 1,190 0 14 Jan 2022
When are Iterative Gaussian Processes Reliably Accurate? Wesley J. Maddox Sanyam Kapoor A. Wilson 35 10 0 31 Dec 2021
GPEX, A Framework For Interpreting Artificial Neural Networks Amir Akbarnejad G. Bigras Nilanjan Ray 52 4 0 18 Dec 2021
BoGraph: Structured Bayesian Optimization From Logs for Expensive Systems with Many Parameters Sami Alabed Eiko Yoneki 23 7 0 16 Dec 2021
Probabilistic Deep Learning to Quantify Uncertainty in Air Quality Forecasting Abdulmajid Murad F. Kraemer Kerstin Bach Gavin Taylor OOD BDL UQCV 25 12 0 05 Dec 2021
Gaussian Process Inference Using Mini-batch Stochastic Gradient Descent: Convergence Guarantees and Empirical Benefits Hao Chen Lili Zheng Raed Al Kontar Garvesh Raskutti 29 3 0 19 Nov 2021
Positional Encoder Graph Neural Networks for Geographic Data Konstantin Klemmer Nathan Safir Daniel B. Neill GNN 30 34 0 19 Nov 2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 25 10 0 08 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 29 20 0 05 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 24 31 0 02 Nov 2021
Geometry-aware Bayesian Optimization in Robotics using Riemannian Matérn Kernels Noémie Jaquier Viacheslav Borovitskiy A. Smolensky Alexander Terenin Tamim Asfour Leonel Rozo 34 35 0 02 Nov 2021
Geometry-Aware Hierarchical Bayesian Learning on Manifolds Yonghui Fan Yalin Wang 18 2 0 30 Oct 2021
Learning Robust Controllers Via Probabilistic Model-Based Policy Search V. Charvet B. S. Jensen R. Murray-Smith 19 2 0 26 Oct 2021
Incremental Ensemble Gaussian Processes Qin Lu G. V. Karanikolas G. Giannakis 55 25 0 13 Oct 2021
Probabilistic Metamodels for an Efficient Characterization of Complex Driving Scenarios Max Winkelmann Mike Kohlhoff H. Tadjine Steffen Müller 34 9 0 06 Oct 2021
Approximate Latent Force Model Inference Jacob Moss Felix L. Opolka Bianca Dumitrascu Pietro Lio 54 3 0 24 Sep 2021
Barely Biased Learning for Gaussian Process Regression David R. Burt A. Artemev Mark van der Wilk 21 0 0 20 Sep 2021
Gaussian Process Uniform Error Bounds with Unknown Hyperparameters for Safety-Critical Applications A. Capone Armin Lederer Sandra Hirche 32 18 0 06 Sep 2021
Adversarial Attack for Uncertainty Estimation: Identifying Critical Regions in Neural Networks Ismail Alarab S. Prakoonwit AAML 27 14 0 15 Jul 2021
Hyperparameter Optimization: Foundations, Algorithms, Best Practices and Open Challenges B. Bischl Martin Binder Michel Lang Tobias Pielok Jakob Richter ... Theresa Ullmann Marc Becker A. Boulesteix Difan Deng Marius Lindauer 85 455 0 13 Jul 2021
Scaling Gaussian Processes with Derivative Information Using Variational Inference Misha Padidar Xinran Zhu Leo Huang Jacob R. Gardner D. Bindel BDL 19 18 0 08 Jul 2021
CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation Y. Tashiro Jiaming Song Yang Song Stefano Ermon BDL DiffM 27 516 0 07 Jul 2021
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization Jonathan Wenger Geoff Pleiss Philipp Hennig John P. Cunningham Jacob R. Gardner 22 24 0 01 Jul 2021