Gaussian Processes for Big Data

26 September 2013

Papers citing "Gaussian Processes for Big Data"

50 / 190 papers shown

Title
Sample-Efficient Optimisation with Probabilistic Transformer Surrogates A. Maraval Matthieu Zimmer Antoine Grosnit Rasul Tutunov Jun Wang H. Ammar 30 2 0 27 May 2022
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel Ziyang Jiang Tongshu Zheng Yiling Liu David Carlson 30 4 0 15 May 2022
Probabilistic Models for Manufacturing Lead Times Recep Yusuf Bekci Yacine Mahdid Jinling Xing Nikita Letov Ying Zhang Zahid Pasha 24 0 0 28 Apr 2022
A piece-wise constant approximation for non-conjugate Gaussian Process models Sarem Seitz 17 0 0 22 Apr 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 38 89 0 23 Mar 2022
Fully Decentralized, Scalable Gaussian Processes for Multi-Agent Federated Learning George P. Kontoudis D. Stilwell FedML 16 8 0 06 Mar 2022
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian Processes Felix Jimenez Matthias Katzfuss 21 10 0 02 Mar 2022
Learning Conditional Variational Autoencoders with Missing Covariates S. Ramchandran Gleb Tikhonov Otto Lönnroth Pekka Tiikkainen Harri Lähdesmäki CML 19 14 0 02 Mar 2022
Video is All You Need: Attacking PPG-based Biometric Authentication Lin Li Chao Chen Lei Pan Jun Zhang Yang Xiang AAML 21 13 0 02 Mar 2022
Learning Multi-Task Gaussian Process Over Heterogeneous Input Domains Haitao Liu Kai Wu Yew-Soon Ong Chao Bian Xiaomo Jiang Xiaofang Wang 19 7 0 25 Feb 2022
AutoIP: A United Framework to Integrate Physics into Gaussian Processes D. Long Zihan Wang Aditi S. Krishnapriyan Robert M. Kirby Shandian Zhe Michael W. Mahoney AI4CE 34 14 0 24 Feb 2022
Partitioned Variational Inference: A Framework for Probabilistic Federated Learning Matthew Ashman T. Bui Cuong V Nguyen Efstratios Markou Adrian Weller S. Swaroop Richard Turner FedML 19 12 0 24 Feb 2022
Bayesian Model Selection, the Marginal Likelihood, and Generalization Sanae Lotfi Pavel Izmailov Gregory W. Benton Micah Goldblum A. Wilson UQCV BDL 52 56 0 23 Feb 2022
Adaptive Cholesky Gaussian Processes Simon Bartels Kristoffer Stensbo-Smidt Pablo Moreno-Muñoz Wouter Boomsma J. Frellsen Søren Hauberg 33 3 0 22 Feb 2022
Improved Convergence Rates for Sparse Approximation Methods in Kernel-Based Learning Sattar Vakili Jonathan Scarlett Da-shan Shiu A. Bernacchia 33 18 0 08 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
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 58 69 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
When are Iterative Gaussian Processes Reliably Accurate? Wesley J. Maddox Sanyam Kapoor A. Wilson 35 10 0 31 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 20 3 0 19 Nov 2021
BOiLS: Bayesian Optimisation for Logic Synthesis Antoine Grosnit C. Malherbe Rasul Tutunov Xingchen Wan Jun Wang H. Ammar 14 30 0 11 Nov 2021
Fast and Scalable Spike and Slab Variable Selection in High-Dimensional Gaussian Processes Hugh Dance Brooks Paige GP 20 10 0 08 Nov 2021
Dual Parameterization of Sparse Variational Gaussian Processes Vincent Adam Paul E. Chang Mohammad Emtiyaz Khan Arno Solin 21 20 0 05 Nov 2021
Spatio-Temporal Variational Gaussian Processes Oliver Hamelijnck William J. Wilkinson Niki A. Loppi Arno Solin Theodoros Damoulas AI4TS 19 31 0 02 Nov 2021
Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels M. Hutchinson Alexander Terenin Viacheslav Borovitskiy So Takao Yee Whye Teh M. Deisenroth 28 20 0 27 Oct 2021
Variational Bayesian Approximation of Inverse Problems using Sparse Precision Matrices Jan Povala Ieva Kazlauskaite Eky Febrianto F. Cirak Mark Girolami 32 22 0 22 Oct 2021
Nonnegative spatial factorization F. W. Townes Barbara E. Engelhardt 16 11 0 12 Oct 2021
Scaling Bayesian Optimization With Game Theory L. Mathesen G. Pedrielli R. L. Smith 19 1 0 07 Oct 2021
Uncertainty Quantification and Experimental Design for Large-Scale Linear Inverse Problems under Gaussian Process Priors Cédric Travelletti D. Ginsbourger N. Linde 30 3 0 08 Sep 2021
Optimal Order Simple Regret for Gaussian Process Bandits Sattar Vakili N. Bouziani Sepehr Jalali A. Bernacchia Da-shan Shiu 31 51 0 20 Aug 2021
PI3NN: Out-of-distribution-aware prediction intervals from three neural networks Si-Yuan Liu Pei Zhang Dan Lu Guannan Zhang OODD 22 10 0 05 Aug 2021
The Bayesian Learning Rule Mohammad Emtiyaz Khan Håvard Rue BDL 63 73 0 09 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
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
Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factors Liwei Wang Suraj Yerramilli Akshay Iyer D. Apley Ping Zhu Wei Chen 38 25 0 26 Jun 2021
Deep Gaussian Processes: A Survey Kalvik Jakkala AI4CE GP BDL 29 19 0 21 Jun 2021
Variational multiple shooting for Bayesian ODEs with Gaussian processes Pashupati Hegde Çağatay Yıldız Harri Lähdesmäki Samuel Kaski Markus Heinonen 30 16 0 21 Jun 2021
Combining Pseudo-Point and State Space Approximations for Sum-Separable Gaussian Processes Will Tebbutt Arno Solin Richard Turner 21 8 0 18 Jun 2021
Deep Probabilistic Time Series Forecasting using Augmented Recurrent Input for Dynamic Systems Haitao Liu Changjun Liu Xiaomo Jiang Xudong Chen Shuhua Yang Xiaofang Wang BDL AI4TS 44 2 0 03 Jun 2021
Scalable Cross Validation Losses for Gaussian Process Models M. Jankowiak Geoff Pleiss 20 6 0 24 May 2021
Priors in Bayesian Deep Learning: A Review Vincent Fortuin UQCV BDL 31 124 0 14 May 2021
Deep Gaussian Processes for Biogeophysical Parameter Retrieval and Model Inversion D. Svendsen Pablo Morales-Álvarez A. Ruescas Rafael Molina Gustau Camps-Valls 30 29 0 16 Apr 2021
High-Dimensional Bayesian Optimization with Multi-Task Learning for RocksDB Sami Alabed Eiko Yoneki 21 17 0 30 Mar 2021
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations Luhuan Wu Andrew C. Miller Lauren Anderson Geoff Pleiss David M. Blei John P. Cunningham 27 8 0 28 Feb 2021
Bias-Free Scalable Gaussian Processes via Randomized Truncations Andres Potapczynski Luhuan Wu D. Biderman Geoff Pleiss John P. Cunningham 10 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
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes A. Benavoli Dario Azzimonti Dario Piga 27 15 0 12 Dec 2020
Exploration in Online Advertising Systems with Deep Uncertainty-Aware Learning Chao Du Zhifeng Gao Shuo Yuan Lining Gao Z. Li Yifan Zeng Xiaoqiang Zhu Jian Xu Kun Gai Kuang-chih Lee 25 18 0 25 Nov 2020
Cluster-Specific Predictions with Multi-Task Gaussian Processes Arthur Leroy Pierre Latouche Benjamin Guedj S. Gey 18 4 0 16 Nov 2020