Gaussian Processes for Big Data

26 September 2013

Papers citing "Gaussian Processes for Big Data"

50 / 190 papers shown

Title
Gradient-based Sample Selection for Faster Bayesian Optimization Qiyu Wei Haowei Wang Zirui Cao Songhao Wang Richard Allmendinger Mauricio A Álvarez 31 0 0 10 Apr 2025
Sparse Gaussian Neural Processes Tommy Rochussen Vincent Fortuin BDL UQCV 61 0 0 02 Apr 2025
Preconditioned Additive Gaussian Processes with Fourier Acceleration Theresa Wagner Tianshi Xu Franziska Nestler Yuanzhe Xi Martin Stoll 54 1 0 01 Apr 2025
Efficient Transformed Gaussian Process State-Space Models for Non-Stationary High-Dimensional Dynamical Systems Zhidi Lin Ying Li Feng Yin Juan Maroñas Alexandre Thiéry 54 0 0 24 Mar 2025
Efficient dynamic modal load reconstruction using physics-informed Gaussian processes based on frequency-sparse Fourier basis functions Gledson Rodrigo Tondo I. Kavrakov Guido Morgenthal 55 2 0 13 Mar 2025
Shared Stochastic Gaussian Process Latent Variable Models: A Multi-modal Generative Model for Quasar Spectra Vidhi Lalchand Anna-Christina Eilers 66 0 0 27 Feb 2025
Bayesian Adaptive Calibration and Optimal Design Rafael Oliveira Dino Sejdinovic David Howard Edwin Bonilla 113 0 0 20 Jan 2025
Scalable Bayesian Optimization via Focalized Sparse Gaussian Processes Yunyue Wei Vincent Zhuang Saraswati Soedarmadji Yanan Sui 179 0 0 31 Dec 2024
Learning from Summarized Data: Gaussian Process Regression with Sample Quasi-Likelihood Yuta Shikuri GP 41 0 0 23 Dec 2024
High-Dimensional Gaussian Process Regression with Soft Kernel Interpolation Chris Camaño Daniel Huang BDL GP 45 1 0 28 Oct 2024
Spectral Representations for Accurate Causal Uncertainty Quantification with Gaussian Processes Hugh Dance Peter Orbanz Arthur Gretton CML 37 1 0 18 Oct 2024
Adaptive RKHS Fourier Features for Compositional Gaussian Process Models Xinxing Shi Thomas Baldwin-McDonald Mauricio A. Álvarez 79 0 0 01 Jul 2024
Robust Inference of Dynamic Covariance Using Wishart Processes and Sequential Monte Carlo Hester Huijsdens D. Leeftink Linda Geerligs Max Hinne 37 0 0 07 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 43 2 0 28 May 2024
Scalable Amortized GPLVMs for Single Cell Transcriptomics Data Sarah Zhao Aditya Ravuri V. Lalchand Neil D. Lawrence BDL VLM 28 0 0 06 May 2024
Attacking Bayes: On the Adversarial Robustness of Bayesian Neural Networks Yunzhen Feng Tim G. J. Rudner Nikolaos Tsilivis Julia Kempe AAML BDL 43 1 0 27 Apr 2024
Preventing Model Collapse in Gaussian Process Latent Variable Models Ying Li Zhidi Lin Feng Yin Michael Minyi Zhang VLM 35 1 0 02 Apr 2024
Explainable Learning with Gaussian Processes Kurt Butler Guanchao Feng P. Djuric 39 1 0 11 Mar 2024
Improving sample efficiency of high dimensional Bayesian optimization with MCMC Zeji Yi Yunyue Wei Chu Xin Cheng Kaibo He Yanan Sui 27 5 0 05 Jan 2024
Robust and Conjugate Gaussian Process Regression Matias Altamirano F. Briol Jeremias Knoblauch 28 10 0 01 Nov 2023
Surrogate modeling for stochastic crack growth processes in structural health monitoring applications Nicholas E. Silionis K. Anyfantis AI4CE 26 0 0 11 Oct 2023
Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes Gledson Rodrigo Tondo Sebastian Rau I. Kavrakov Guido Morgenthal 35 6 0 21 Sep 2023
Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models Frederiek Wesel Kim Batselier 24 1 0 11 Sep 2023
Parallel and Limited Data Voice Conversion Using Stochastic Variational Deep Kernel Learning Mohamadreza Jafaryani H. Sheikhzadeh V. Pourahmadi 19 4 0 08 Sep 2023
Memory-Based Dual Gaussian Processes for Sequential Learning Paul E. Chang Prakhar Verma S. T. John Arno Solin Mohammad Emtiyaz Khan GP 25 4 0 06 Jun 2023
Vecchia Gaussian Process Ensembles on Internal Representations of Deep Neural Networks Felix Jimenez Matthias Katzfuss BDL UQCV 64 1 0 26 May 2023
Privacy-aware Gaussian Process Regression Rui Tuo R. Bhattacharya 14 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 28 2 0 25 May 2023
Actually Sparse Variational Gaussian Processes Harry Jake Cunningham Daniel Augusto R. M. A. de Souza So Takao Mark van der Wilk M. Deisenroth 32 5 0 11 Apr 2023
Self-Distillation for Gaussian Process Regression and Classification Kenneth Borup L. Andersen 11 2 0 05 Apr 2023
Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan Edwin V. Bonilla T. O’Kane Scott A. Sisson 16 9 0 20 Feb 2023
Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes Ba-Hien Tran Babak Shahbaba Stephan Mandt Maurizio Filippone SyDa BDL UQCV 21 5 0 09 Feb 2023
Inducing Point Allocation for Sparse Gaussian Processes in High-Throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 32 24 0 24 Jan 2023
Towards Flexibility and Interpretability of Gaussian Process State-Space Model Zhidi Lin Feng Yin Juan Maroñas 34 7 0 21 Jan 2023
Robust Bayesian Target Value Optimization J. G. Hoffer Sascha Ranftl Bernhard C. Geiger 25 9 0 11 Jan 2023
Counterfactual Learning with Multioutput Deep Kernels A. Caron G. Baio I. Manolopoulou BDL CML OffRL 25 1 0 20 Nov 2022
ET-AL: Entropy-Targeted Active Learning for Bias Mitigation in Materials Data Hengrui Zhang Wei Chen J. Rondinelli Wei Chen AI4CE 19 17 0 15 Nov 2022
Safe and Adaptive Decision-Making for Optimization of Safety-Critical Systems: The ARTEO Algorithm Buse Sibel Korkmaz Marta Zagórowska Mehmet Mercangöz 27 2 0 10 Nov 2022
Locally Smoothed Gaussian Process Regression Davit Gogolashvili B. Kozyrskiy Maurizio Filippone 30 8 0 18 Oct 2022
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees Alexander Terenin David R. Burt A. Artemev Seth Flaxman Mark van der Wilk C. Rasmussen Hong Ge 58 7 0 14 Oct 2022
Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture M. Jung He Zhao Joanna Dipnall Belinda Gabbe Lan Du UQCV EDL 59 12 0 06 Oct 2022
Physically Meaningful Uncertainty Quantification in Probabilistic Wind Turbine Power Curve Models as a Damage Sensitive Feature J. H. Mclean Matthew R. Jones Brandon J. O'Connell Eoghan Maguire T. Rogers 21 6 0 30 Sep 2022
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing Ruofeng Wen Wenjun Zeng Yi Liu 24 0 0 08 Sep 2022
Variational Inference for Model-Free and Model-Based Reinforcement Learning Felix Leibfried OffRL 15 0 0 04 Sep 2022
Fast emulation of density functional theory simulations using approximate Gaussian processes S. Stetzler M. Grosskopf E. Lawrence 23 0 0 24 Aug 2022
Bayesian Optimization with Informative Covariance Afonso Eduardo Michael U. Gutmann 24 3 0 04 Aug 2022
Collaborative Learning in Kernel-based Bandits for Distributed Users Sudeep Salgia Sattar Vakili Qing Zhao FedML 37 6 0 16 Jul 2022
Low Emission Building Control with Zero-Shot Reinforcement Learning Scott Jeen Alessandro Abate Jonathan M. Cullen AI4CE 19 5 0 28 Jun 2022
Information-theoretic Inducing Point Placement for High-throughput Bayesian Optimisation Henry B. Moss Sebastian W. Ober Victor Picheny 24 4 0 06 Jun 2022
Efficient Transformed Gaussian Processes for Non-Stationary Dependent Multi-class Classification Juan Maroñas Daniel Hernández-Lobato 19 6 0 30 May 2022