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Harnessing Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization [article]

Bin Liu
2019 arXiv   pre-print
We explore the idea of posterior regularization to harness low fidelity (LF) data within the Gaussian process upper confidence bound (GP-UCB) framework.  ...  The regularization is induced by this operator on the posterior of the BOF. The impact of the LF GP model on the resulting regularized posterior is adaptively adjusted via Bayesian formalism.  ...  In particular, we explore the idea of posterior regularization to accelerate the GP-UCB method of [1] by harnessing LF data.  ... 
arXiv:1902.03740v5 fatcat:sro7envc4vcbhhds5aptjbw5ii

Table of contents

2020 2020 IEEE International Conference on Big Data and Smart Computing (BigComp)  
Low-Fidelity Data to Accelerate Bayesian Optimization via Posterior Regularization 140 Bin Liu (Nanjing University of Posts and Telecommunications) Machine Learning II Learning How Spectator Reactions  ...  Analysis II An AHP/TOPSIS-Based Approach for an Optimal Site Selection of a Commercial Opening Interactive Analytics of Massive Spatial Vector Data via Display-Driven Computing 311 Mengyu Ma (National  ... 
doi:10.1109/bigcomp48618.2020.00004 fatcat:h26admm4frb7nfxm3ofn4et4ni

Learning Quantum Systems [article]

Valentin Gebhart, Raffaele Santagati, Antonio Andrea Gentile, Erik Gauger, David Craig, Natalia Ares, Leonardo Banchi, Florian Marquardt, Luca Pezze', Cristian Bonato
2022 arXiv   pre-print
This review provides a brief background for key concepts recurring across many of these approaches, such as the Bayesian formalism or Neural Networks, and outlines open questions.  ...  Here, we review classical approaches to learning quantum systems, their correlation properties, their dynamics and their interaction with the environment.  ...  In a Bayesian setting (see Box A), Eq. ( 7 ) provides the asymptotic posterior variance using optimal measurements.  ... 
arXiv:2207.00298v2 fatcat:mbkttj4hnrfxbnhvbf4s64gube

Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems [article]

Yibo Yang, Paris Perdikaris
2019 arXiv   pre-print
The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation  ...  We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems.  ...  Clearly, the appropriate utilization of the low-fidelity data results in significant accuracy gains for the multi-fidelity case, while the single-fidelity model is not able to generalize well and suffers  ... 
arXiv:1901.04878v1 fatcat:7vqb4qdrebdfpf5hlansajzgxa

A surrogate-model assisted approach for optimising the size of tidal turbine arrays

D.M. Culley, S.W. Funke, S.C. Kramer, M.D. Piggott
2017 International Journal of Marine Energy  
Both have been shown to significantly impact upon the energy yield and profitability of an array.  ...  The new and costly nature of tidal stream energy extraction technologies can lead to narrow margins of success for a project.  ...  Finally, the authors would like to acknowledge helpful discussions with T. Griffiths and B. Shuttleworth.  ... 
doi:10.1016/j.ijome.2017.05.001 fatcat:3nswyspbxzb4tkbhddo5mczqxa

Deep Coregionalization for the Emulation of Spatial-Temporal Fields [article]

Wei Xing, Robert M. Kirby, Shandian Zhe
2019 arXiv   pre-print
To address this issue, we exploit the multi-fidelity nature of a PDE simulator and introduce deep coregionalization, a Bayesian non-parametric autoregressive framework for efficient emulation of spatial-temporal  ...  To effectively extract the output correlations in the context of multi-fidelity data, we develop a novel dimension reduction technique, residual principal component analysis.  ...  Authors would like to thank Kyli McKay-Bishop for proofreading the manuscript. References  ... 
arXiv:1910.07577v1 fatcat:kjhsic4ktvc6xa4onzwqyammm4

Surrogate-assisted parallel tempering for Bayesian neural learning [article]

Rohitash Chandra, Konark Jain, Arpit Kapoor, Ashray Aman
2020 arXiv   pre-print
Due to the need for robust uncertainty quantification, Bayesian neural learning has gained attention in the era of deep learning and big data.  ...  Hence, we present surrogate-assisted parallel tempering for Bayesian neural learning for simple to computationally expensive models.  ...  Acknowledgement The authors would like to thank Prof. Dietmar Muller and Danial Azam for discussions and support during the course of this research project.  ... 
arXiv:1811.08687v3 fatcat:yzsduvrojjaajihutzyrcnz5fy

Robust Sequential Online Prediction with Dynamic Ensemble of Multiple Models: A Concise Introduction [article]

Bin Liu
2022 arXiv   pre-print
This framework has three major features: (1) it employs a model pool, rather than a single model, to capture possible statistical regularities underlying the data; (2) the model pool consists of multiple  ...  weighted candidate models, wherein the model weights are adapted online to capture possible temporal evolutions of the data; (3) the adaptation for the model weights follows Bayesian formalism.  ...  Bayesian Optimization An INTEL type BDEMM algorithm, termed accelerated Bayesian Optimization (ABO), is proposed to address the following question: how to make use of low-fidelity (LF) data to accelerate  ... 
arXiv:2112.02374v3 fatcat:gjquo6l7srgi5er2yoye5txqwe

2021 Index IEEE Signal Processing Letters Vol. 28

2021 IEEE Signal Processing Letters  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  Li, Y., +, LSP 2021 1345-1349 Data reduction Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank Estimation.  ...  ., LSP 2021 1963-1967 Generalizing AUC Optimization to Multiclass Classification for Audio Seg-mentation With Limited Training Data.  ... 
doi:10.1109/lsp.2022.3145253 fatcat:a3xqvok75vgepcckwnhh2mty74

On Provably Robust Meta-Bayesian Optimization [article]

Zhongxiang Dai, Yizhou Chen, Haibin Yu, Bryan Kian Hsiang Low, Patrick Jaillet
2022 arXiv   pre-print
Bayesian optimization (BO) has become popular for sequential optimization of black-box functions.  ...  We also exploit the theoretical guarantees to optimize the weights assigned to individual previous tasks through regret minimization via online learning, which diminishes the impact of dissimilar tasks  ...  ,T , λ is a regularization parameter. Meta-Bayesian Optimization.  ... 
arXiv:2206.06872v2 fatcat:3fpucgdkdjdxtcpc2zopdfi7b4

Rapid multi-orientation quantitative susceptibility mapping

Berkin Bilgic, Luke Xie, Russell Dibb, Christian Langkammer, Aysegul Mutluay, Huihui Ye, Jonathan R. Polimeni, Jean Augustinack, Chunlei Liu, Lawrence L. Wald, Kawin Setsompop
2016 NeuroImage  
NeuroImage 102, 748-755. bayesian regularization: validation and application to brain imaging. Magn. Reson.  ...  Since the tight-fitting head coil allowed only 257 minor head rotations, a Tikhonov penalty was added to mitigate the re-258 sidual streaking artifacts via the regularizer R(χ) = ‖χ‖ 2 2 with regular-259  ... 
doi:10.1016/j.neuroimage.2015.08.015 pmid:26277773 pmcid:PMC4691433 fatcat:gv7jsgjixbcpffiqjcgltvisqa

Table of Contents

2021 IEEE Signal Processing Letters  
Liao Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  ...  Sun Partially Linear Bayesian Estimation Using Mixed-Resolution Data . . . . . . . . . . . . . . . . . . I. E. Berman and T.  ... 
doi:10.1109/lsp.2021.3134549 fatcat:m6obtl7k7zdqvd62eo3c4tptfy

A Survey of Uncertainty in Deep Neural Networks [article]

Jakob Gawlikowski, Cedrique Rovile Njieutcheu Tassi, Mohsin Ali, Jongseok Lee, Matthias Humt, Jianxiang Feng, Anna Kruspe, Rudolph Triebel, Peter Jung, Ribana Roscher, Muhammad Shahzad, Wen Yang (+2 others)
2022 arXiv   pre-print
The modeling of these uncertainties based on deterministic neural networks, Bayesian neural networks, ensemble of neural networks, and test-time data augmentation approaches is introduced and different  ...  A comprehensive introduction to the most crucial sources of uncertainty is given and their separation into reducible model uncertainty and not reducible data uncertainty is presented.  ...  evaluated to be low.  ... 
arXiv:2107.03342v3 fatcat:cex5j3xq5fdijjdtdbt2ixralm

Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection

Kaiguang Zhao, Denis Valle, Sorin Popescu, Xuesong Zhang, Bani Mallick
2013 Remote Sensing of Environment  
Computationally, the model calibration with datasets of moderate sizes (>100) was faster for BMA via a hybrid reversible-jump Monte Carlo Markov Chain sampler than for PLS via literal optimization of a  ...  Our BMA scheme also provides a generic hierarchical Bayesian framework to assimilate prior knowledge of diverse forms, as illustrated by its use to account for nonlinearity in spectral-chemical relationships  ...  Anatoly Gitelson at the University of Nebraska-Lincoln who generously shared his maize and maple spectral-chemical data.  ... 
doi:10.1016/j.rse.2012.12.026 fatcat:nfib3obnuzdevitawjnman4oti

2022 Review of Data-Driven Plasma Science [article]

Rushil Anirudh, Rick Archibald, M. Salman Asif, Markus M. Becker, Sadruddin Benkadda, Peer-Timo Bremer, Rick H.S. Budé, C.S. Chang, Lei Chen, R. M. Churchill, Jonathan Citrin, Jim A Gaffney (+51 others)
2022 arXiv   pre-print
Data science and technology offer transformative tools and methods to science.  ...  It is now becoming impractical for humans to analyze all the data manually.  ...  for ICF Challenge Opportunity Relatively few experiments Model-based design Costly simulations Surrogate-enhanced optimization, multi-fidelity optimization High-dimensional design spaces Bayesian optimization  ... 
arXiv:2205.15832v1 fatcat:fxsl6gl3fncnhpoj76defxoc3a
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