Greedy myopic Bayesian active learning for linear regression achieves risk within a factor linear in the maximum initial leverage score of optimal, and this factor is tight.
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4 Pith papers cite this work. Polarity classification is still indexing.
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Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
A cost-aware space-filling input design method using Gaussian processes for nonlinear system identification that reduces experimental cost while preserving model performance.
Forward sensitivity analysis via Gaussian process emulators identifies observation regions that serve as strong proxies for accurate Bayesian parameter calibration and reduced posterior uncertainty in Earth system models.
citing papers explorer
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The Approximation Ratio for the Risk of Myopic Bayesian Active Learning for Linear Regression
Greedy myopic Bayesian active learning for linear regression achieves risk within a factor linear in the maximum initial leverage score of optimal, and this factor is tight.
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Response Time Enhances Alignment with Heterogeneous Preferences
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
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Least Costly Space-Filling Experiment Design for the Identification of a Nonlinear System
A cost-aware space-filling input design method using Gaussian processes for nonlinear system identification that reduces experimental cost while preserving model performance.
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Connecting the forward problem to the inverse problem in uncertainty quantification of Earth system models using fast emulators
Forward sensitivity analysis via Gaussian process emulators identifies observation regions that serve as strong proxies for accurate Bayesian parameter calibration and reduced posterior uncertainty in Earth system models.