Gaussian Processes for Big Data

26 September 2013

Papers citing "Gaussian Processes for Big Data"

50 / 604 papers shown

Title
Surrogate modeling for stochastic crack growth processes in structural health monitoring applications Nicholas E. Silionis K. Anyfantis AI4CE 74 0 0 11 Oct 2023
Gradient and Uncertainty Enhanced Sequential Sampling for Global Fit Sven Lämmle Can Bogoclu K. Cremanns D. Roos 62 5 0 29 Sep 2023
Pointwise uncertainty quantification for sparse variational Gaussian process regression with a Brownian motion prior Luke Travis Kolyan Ray 77 4 0 29 Sep 2023
Neural Operator Variational Inference based on Regularized Stein Discrepancy for Deep Gaussian Processes Jian Xu Shian Du Junmei Yang Qianli Ma Delu Zeng BDL 52 1 0 22 Sep 2023
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes Gledson Rodrigo Tondo Sebastian Rau I. Kavrakov Guido Morgenthal 94 6 0 21 Sep 2023
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models Frederiek Wesel Kim Batselier 77 1 0 11 Sep 2023
Generalized Variable Selection Algorithms for Gaussian Process Models by LASSO-like Penalty Zhiyong Hu D. Dey 69 3 0 08 Sep 2023
Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning Mohamadreza Jafaryani H. Sheikhzadeh V. Pourahmadi 76 4 0 08 Sep 2023
Sparse Function-space Representation of Neural Networks Aidan Scannell Riccardo Mereu Paul E. Chang Ella Tamir Joni Pajarinen Arno Solin BDL 75 1 0 05 Sep 2023
Towards Efficient Modeling and Inference in Multi-Dimensional Gaussian Process State-Space Models Zhidi Lin Juan Maroñas Ying Li Feng Yin Sergios Theodoridis 93 3 0 03 Sep 2023
A Unifying Variational Framework for Gaussian Process Motion Planning Lucas Cosier Rares Iordan Sicelukwanda Zwane Giovanni Franzese James T. Wilson M. Deisenroth Alexander Terenin Yasemin Bekiroglu 3DV 81 4 0 02 Sep 2023
Dynamic Factor Analysis with Dependent Gaussian Processes for High-Dimensional Gene Expression Trajectories Jiachen Cai Robert J. B. Goudie Colin Starr Brian D. M. Tom 26 1 0 06 Jul 2023
Data-Driven Design for Metamaterials and Multiscale Systems: A Review Doksoo Lee Wei Chen Liwei Wang Yu-Chin Chan Wei Chen AI4CE 67 85 0 01 Jul 2023
Tanimoto Random Features for Scalable Molecular Machine Learning Austin Tripp S. Bacallado Sukriti Singh José Miguel Hernández-Lobato 74 8 0 26 Jun 2023
Leveraging Locality and Robustness to Achieve Massively Scalable Gaussian Process Regression Robert Allison Anthony Stephenson F. Samuel Edward O. Pyzer-Knapp UQCV 68 4 0 26 Jun 2023
Uncertainty Estimation for Molecules: Desiderata and Methods Tom Wollschlager Nicholas Gao Bertrand Charpentier Mohamed Amine Ketata Stephan Günnemann 93 10 0 20 Jun 2023
Sampling from Gaussian Process Posteriors using Stochastic Gradient Descent J. Lin Javier Antorán Shreyas Padhy David Janz José Miguel Hernández-Lobato Alexander Terenin 110 25 0 20 Jun 2023
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High Dimensions Jonathan Schmidt Philipp Hennig Jorg Nick Filip Tronarp 87 11 0 13 Jun 2023
Improving Hyperparameter Learning under Approximate Inference in Gaussian Process Models Rui Li S. T. John Arno Solin BDL 61 3 0 07 Jun 2023
Rao-Blackwellized Particle Smoothing for Simultaneous Localization and Mapping Manon Kok Arno Solin Thomas B. Schon 56 6 0 06 Jun 2023
Memory-Based Dual Gaussian Processes for Sequential Learning Paul E. Chang Prakhar Verma S. T. John Arno Solin Mohammad Emtiyaz Khan GP 95 8 0 06 Jun 2023
Bivariate Causal Discovery using Bayesian Model Selection Anish Dhir Samuel Power Mark van der Wilk CML 78 3 0 05 Jun 2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez Matthias Katzfuss BDL UQCV 109 1 0 26 May 2023
Privacy-aware Gaussian Process Regression Rui Tuo R. Bhattacharya 66 1 0 25 May 2023
Inverse Protein Folding Using Deep Bayesian Optimization Natalie Maus Yimeng Zeng Daniel A. Anderson Phillip M. Maffettone Aaron C. Solomon Peyton Greenside Osbert Bastani Jacob R. Gardner 101 3 0 25 May 2023
Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis Kim Jie Koh F. Cirak AI4CE 67 12 0 23 May 2023
$Efficient and Interpretable Additive Gaussian Process Regression and Application to Analysis of Hourly-recorded $\text{NO}_2$ Concentrations in London$ Efficient and Interpretable Additive Gaussian Process Regression and Application to Analysis of Hourly-recorded $\text{NO}_2$ Concentrations in London Sahoko Ishida Wicher P. Bergsma 27 0 0 11 May 2023
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes Louis C. Tiao Vincent Dutordoir Victor Picheny BDL 64 0 0 27 Apr 2023
Actually Sparse Variational Gaussian Processes Harry Jake Cunningham Daniel Augusto R. M. A. de Souza So Takao Mark van der Wilk M. Deisenroth 89 7 0 11 Apr 2023
Self-Distillation for Gaussian Process Regression and Classification Kenneth Borup L. Andersen 59 2 0 05 Apr 2023
Sparse Gaussian Processes with Spherical Harmonic Features Revisited Stefanos Eleftheriadis Dominic Richards J. Hensman 76 1 0 28 Mar 2023
Kernel Regression with Infinite-Width Neural Networks on Millions of Examples Ben Adlam Jaehoon Lee Shreyas Padhy Zachary Nado Jasper Snoek 82 12 0 09 Mar 2023
Calibrating Transformers via Sparse Gaussian Processes Wenlong Chen Yingzhen Li UQCV 119 12 0 04 Mar 2023
Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes M. Ross Markus Heinonen 52 2 0 03 Mar 2023
Efficient Sensor Placement from Regression with Sparse Gaussian Processes in Continuous and Discrete Spaces Kalvik Jakkala Srinivas Akella 71 1 0 28 Feb 2023
Variational Linearized Laplace Approximation for Bayesian Deep Learning Luis A. Ortega Simón Rodríguez Santana Daniel Hernández-Lobato BDL UQCV 135 4 0 24 Feb 2023
Adaptive Sparse Gaussian Process Vanessa Gómez-Verdejo Emilio Parrado-Hernández M. Martínez‐Ramón 44 7 0 20 Feb 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 75 9 0 20 Feb 2023
Guided Deep Kernel Learning Idan Achituve Gal Chechik Ethan Fetaya BDL 66 7 0 19 Feb 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes Ba-Hien Tran Babak Shahbaba Stephan Mandt Maurizio Filippone SyDa BDL UQCV 81 7 0 09 Feb 2023
Learning Choice Functions with Gaussian Processes A. Benavoli Dario Azzimonti Dario Piga 62 5 0 01 Feb 2023
Variational sparse inverse Cholesky approximation for latent Gaussian processes via double Kullback-Leibler minimization JIAN-PENG Cao Myeongjong Kang Felix Jimenez H. Sang Florian Schäfer Matthias Katzfuss 73 7 0 30 Jan 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 110 26 0 24 Jan 2023
An iterative multi-fidelity approach for model order reduction of multi-dimensional input parametric PDE systems Manisha Chetry D. Borzacchiello Lucas Lestandi Luisa Rocha-Da-Silva 44 0 0 23 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 117 7 0 21 Jan 2023
Scalable Gaussian Process Inference with Stan Till Hoffmann J. Onnela GP 35 4 0 21 Jan 2023
Robust Bayesian Target Value Optimization J. G. Hoffer Sascha Ranftl Bernhard C. Geiger 79 10 0 11 Jan 2023
Output-Dependent Gaussian Process State-Space Model Zhidi Lin Lei Cheng Feng Yin Le Xu Shuguang Cui UQCV 87 5 0 15 Dec 2022
GAUCHE: A Library for Gaussian Processes in Chemistry Ryan-Rhys Griffiths Leo Klarner Henry B. Moss Aditya Ravuri Sang T. Truong ... A. Lee Bingqing Cheng Alán Aspuru-Guzik P. Schwaller Jian Tang GP 89 44 0 06 Dec 2022
Counterfactual Learning with Multioutput Deep Kernels A. Caron G. Baio I. Manolopoulou BDL CML OffRL 79 1 0 20 Nov 2022