Active Learning for Deep Gaussian Process Surrogates

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Active Learning for Deep Gaussian Process Surrogates

15 December 2020

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Papers citing "Active Learning for Deep Gaussian Process Surrogates"

12 / 12 papers shown

Title
Active Learning for Multiple Change Point Detection in Non-stationary Time Series with Deep Gaussian Processes Hao Zhao Rong Pan 15 0 0 26 May 2025
STRIDE: Sparse Techniques for Regression in Deep Gaussian Processes Simon Urbainczyk Aretha L. Teckentrup Jonas Latz GP 113 0 0 16 May 2025
Future Aware Safe Active Learning of Time Varying Systems using Gaussian Processes Markus Lange-Hegermann Christoph Zimmer AI4TS 131 0 0 17 May 2024
Inverse Models for Estimating the Initial Condition of Spatio-Temporal Advection-Diffusion Processes Xiao Liu K. Yeo 60 3 0 08 Feb 2023
Active Learning of Piecewise Gaussian Process Surrogates Chiwoo Park R. Waelder Bonggwon Kang Benji Maruyama Soondo Hong R. Gramacy GP 65 2 0 20 Jan 2023
Active sampling: A machine-learning-assisted framework for finite population inference with optimal subsamples Henrik Imberg Xiaomi Yang Carol Flannagan Jonas Bärgman 95 10 0 20 Dec 2022
Bayesian Active Learning with Fully Bayesian Gaussian Processes Christoffer Riis Francisco Antunes F. B. Hüttel C. L. Azevedo Francisco Câmara Pereira GP 96 24 0 20 May 2022
Triangulation candidates for Bayesian optimization R. Gramacy Anna Sauer Nathan Wycoff 118 15 0 14 Dec 2021
A Sparse Expansion For Deep Gaussian Processes Liang Ding Rui Tuo Shahin Shahrampour 48 6 0 11 Dec 2021
Deep Gaussian Process Emulation using Stochastic Imputation Deyu Ming D. Williamson S. Guillas 49 30 0 04 Jul 2021
Sensitivity Prewarping for Local Surrogate Modeling Nathan Wycoff M. Binois R. Gramacy 46 10 0 15 Jan 2021
Sequential Design of Computer Experiments with Quantitative and Qualitative Factors in Applications to HPC Performance Optimization Xia Cai Li Xu C. D. Lin Yili Hong Xinwei Deng 35 0 0 06 Jan 2021