Gaussian Processes for Big Data

26 September 2013

Papers citing "Gaussian Processes for Big Data"

50 / 604 papers shown

Title
DKL-KAN: Scalable Deep Kernel Learning using Kolmogorov-Arnold Networks Shrenik Zinage Sudeepta Mondal S. Sarkar 107 7 0 30 Jul 2024
Scalable Multi-Output Gaussian Processes with Stochastic Variational Inference Xiaoyu Jiang Sokratia Georgaka Magnus Rattray Mauricio A. Alvarez 82 0 0 02 Jul 2024
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Xinxing Shi Thomas Baldwin-McDonald Mauricio A. Álvarez 170 0 0 01 Jul 2024
Quantifying Local Model Validity using Active Learning Sven Lämmle Can Bogoclu Robert Voßhall Anselm Haselhoff Dirk Roos 75 0 0 11 Jun 2024
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo Hester Huijsdens D. Leeftink Linda Geerligs Max Hinne 73 0 0 07 Jun 2024
POAM: Probabilistic Online Attentive Mapping for Efficient Robotic Information Gathering Weizhe (Wesley) Chen Lantao Liu Roni Khardon 68 1 0 06 Jun 2024
Regularized KL-Divergence for Well-Defined Function-Space Variational Inference in Bayesian neural networks Tristan Cinquin Robert Bamler UQCV BDL 132 2 0 06 Jun 2024
Approximation-Aware Bayesian Optimization Natalie Maus Kyurae Kim Geoff Pleiss David Eriksson John P. Cunningham Jacob R. Gardner 69 3 0 06 Jun 2024
Understanding Stochastic Natural Gradient Variational Inference Kaiwen Wu Jacob R. Gardner BDL 89 2 0 04 Jun 2024
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec Javier Antorán José Miguel Hernández-Lobato 135 3 0 28 May 2024
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes J. Lin Shreyas Padhy Bruno Mlodozeniec José Miguel Hernández-Lobato 72 1 0 28 May 2024
Efficient Two-Stage Gaussian Process Regression Via Automatic Kernel Search and Subsampling Shifan Zhao Jiaying Lu Carl Yang Edmond Chow Yuanzhe Xi 107 1 0 22 May 2024
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data Sarah Zhao Aditya Ravuri V. Lalchand Neil D. Lawrence BDL VLM 55 0 0 06 May 2024
Accelerating Convergence in Bayesian Few-Shot Classification Tianjun Ke Haoqun Cao Feng Zhou 103 0 0 02 May 2024
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng Tim G. J. Rudner Nikolaos Tsilivis Julia Kempe AAML BDL 126 2 0 27 Apr 2024
Variational Bayesian surrogate modelling with application to robust design optimisation Thomas A. Archbold Ieva Kazlauskaite F. Cirak 111 1 0 23 Apr 2024
Variational Bayesian Last Layers James Harrison John Willes Jasper Snoek BDL UQCV 149 34 0 17 Apr 2024
Preventing Model Collapse in Gaussian Process Latent Variable Models Ying Li Zhidi Lin Feng Yin Michael Minyi Zhang VLM 64 1 0 02 Apr 2024
Tensor Network-Constrained Kernel Machines as Gaussian Processes Frederiek Wesel Kim Batselier 127 0 0 28 Mar 2024
Function-space Parameterization of Neural Networks for Sequential Learning Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir Joni Pajarinen Arno Solin BDL 92 5 0 16 Mar 2024
Neural-Kernel Conditional Mean Embeddings Eiki Shimizu Kenji Fukumizu Dino Sejdinovic 50 4 0 16 Mar 2024
Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning Gilhyun Ryou Geoffrey Wang S. Karaman 108 3 0 13 Mar 2024
Explainable Learning with Gaussian Processes Kurt Butler Guanchao Feng Petar M. Djurić 123 2 0 11 Mar 2024
Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning Jörn Tebbe Christoph Zimmer A. Steland Markus Lange-Hegermann Fabian Mies GP 92 3 0 28 Feb 2024
Sparse Variational Contaminated Noise Gaussian Process Regression with Applications in Geomagnetic Perturbations Forecasting Daniel Iong Matthew McAnear Yuezhou Qu S. Zou Gabor Toth Yang Chen 44 0 0 27 Feb 2024
Recommendations for Baselines and Benchmarking Approximate Gaussian Processes Sebastian W. Ober A. Artemev Marcel Wagenlander Rudolfs Grobins Mark van der Wilk GP 47 1 0 15 Feb 2024
Latent variable model for high-dimensional point process with structured missingness Maksim Sinelnikov Manuel Haussmann Harri Lähdesmäki 44 1 0 08 Feb 2024
Voronoi Candidates for Bayesian Optimization Nathan Wycoff John W. Smith Annie S. Booth R. Gramacy 90 2 0 07 Feb 2024
Combining additivity and active subspaces for high-dimensional Gaussian process modeling M. Binois Victor Picheny 90 0 0 06 Feb 2024
A Bayesian Gaussian Process-Based Latent Discriminative Generative Decoder (LDGD) Model for High-Dimensional Data Navid Ziaei Behzad Nazari Uri T. Eden A. Widge Ali Yousefi 45 3 0 29 Jan 2024
Efficient Nonparametric Tensor Decomposition for Binary and Count Data Zerui Tao Toshihisa Tanaka Qibin Zhao 78 2 0 15 Jan 2024
Improving sample efficiency of high dimensional Bayesian optimization with MCMC Zeji Yi Yunyue Wei Chu Xin Cheng Kaibo He Yanan Sui 66 6 0 05 Jan 2024
Generative Posterior Networks for Approximately Bayesian Epistemic Uncertainty Estimation Melrose Roderick Felix Berkenkamp Fatemeh Sheikholeslami Zico Kolter UQCV 34 0 0 29 Dec 2023
Adaptation using spatially distributed Gaussian Processes Botond Szabó Amine Hadji A. van der Vaart 59 2 0 21 Dec 2023
A Kronecker product accelerated efficient sparse Gaussian Process (E-SGP) for flow emulation Yu Duan M. Eaton Michael Bluck 58 0 0 13 Dec 2023
Ensemble Kalman Filtering Meets Gaussian Process SSM for Non-Mean-Field and Online Inference Zhidi Lin Yiyong Sun Feng Yin Alexandre Thiéry 84 4 0 10 Dec 2023
Identifiable Feature Learning for Spatial Data with Nonlinear ICA Hermanni Hälvä Jonathan So Richard Turner Aapo Hyvarinen CML 122 3 0 28 Nov 2023
Gaussian Processes for Monitoring Air-Quality in Kampala Clara Stoddart Lauren Shrack Richard Sserunjogi Usman Abdul-Ganiy Engineer Bainomugisha Deo Okure Ruth Misener Jose Pablo Folch Ruby Sedgwick 45 1 0 28 Nov 2023
Deep Latent Force Models: ODE-based Process Convolutions for Bayesian Deep Learning Thomas Baldwin-McDonald Mauricio A. Álvarez 104 1 0 24 Nov 2023
Variational Elliptical Processes Maria B˙ankestad Jens Sjölund Jalil Taghia Thomas B. Schon 89 2 0 21 Nov 2023
Robust and Conjugate Gaussian Process Regression Matias Altamirano F. Briol Jeremias Knoblauch 89 13 0 01 Nov 2023
Stochastic Gradient Descent for Gaussian Processes Done Right J. Lin Shreyas Padhy Javier Antorán Austin Tripp Alexander Terenin Csaba Szepesvári José Miguel Hernández-Lobato David Janz 89 11 0 31 Oct 2023
Dynamic Tensor Decomposition via Neural Diffusion-Reaction Processes Zheng Wang Shikai Fang Shibo Li Shandian Zhe 41 3 0 30 Oct 2023
The Memory Perturbation Equation: Understanding Model's Sensitivity to Data Peter Nickl Lu Xu Dharmesh Tailor Thomas Möllenhoff Mohammad Emtiyaz Khan 72 11 0 30 Oct 2023
Deep Transformed Gaussian Processes Francisco Javier Sáez-Maldonado Juan Maroñas Daniel Hernández-Lobato 95 0 0 27 Oct 2023
Function Space Bayesian Pseudocoreset for Bayesian Neural Networks Balhae Kim Hyungi Lee Juho Lee BDL 60 3 0 27 Oct 2023
Large-Scale Gaussian Processes via Alternating Projection Kaiwen Wu Jonathan Wenger Haydn Thomas Jones Geoff Pleiss Jacob R. Gardner 100 9 0 26 Oct 2023
UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation Ilia Azizi GP 26 0 0 23 Oct 2023
Optimising Distributions with Natural Gradient Surrogates Jonathan So Richard Turner 43 1 0 18 Oct 2023
Pseudo-Bayesian Optimization Haoxian Chen Henry Lam 116 2 0 15 Oct 2023