A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
and Pleiss, Geoff and Bindel, David and Weinberger, Kilian Q
8 Pith papers cite this work, alongside 539 external citations. Polarity classification is still indexing.
8
Pith papers citing it
539
external citations · external index
citation-role summary
method 2
dataset 1
citation-polarity summary
representative citing papers
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
MAESTRO couples surrogate optimization transport modeling with external solvers to enable efficient full-physics steady-state plasma predictions in fusion devices.
A Gaussian Process Regression model trained on an archive of eccentricity-reduced binary black hole simulations predicts initial conditions that achieve low eccentricity with zero or one iteration.
Multi-fidelity bandits screen 529 ZnO co-dopants with 81% fewer DFT calls, identify Y2Cu2 co-doped ZnO (1.84 eV) as optimal for visible-light band gaps, and release all 583 calculations plus code.
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.
Tutorial on a GP-based framework for preference and choice learning that unifies random utility models, limits of discernment, and multi-utility scenarios via customized likelihoods for object and label preferences.
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
citing papers explorer
-
Constrained Bayesian Optimisation with Multiple Information Sources
A multi-source extension of constrained Max-value Entropy Search for Bayesian optimization incorporates auxiliary data sources to improve early exploration and performance under constraints even with weak correlations.
-
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.
-
Accelerating integrated modeling with surrogate-based optimization: the MAESTRO workflow
MAESTRO couples surrogate optimization transport modeling with external solvers to enable efficient full-physics steady-state plasma predictions in fusion devices.
-
Data-Driven Acceleration of Eccentricity Reduction for Binary Black Hole Simulations
A Gaussian Process Regression model trained on an archive of eccentricity-reduced binary black hole simulations predicts initial conditions that achieve low eccentricity with zero or one iteration.
-
Accelerated Dopant Screening in Oxide Semiconductors via Multi-Fidelity Contextual Bandits and a Three-Tier DFT Validation Funnel
Multi-fidelity bandits screen 529 ZnO co-dopants with 81% fewer DFT calls, identify Y2Cu2 co-doped ZnO (1.84 eV) as optimal for visible-light band gaps, and release all 583 calculations plus code.
-
Physics-informed automated surface reconstructing via low-energy electron diffraction based on Bayesian optimization
A trust-region Bayesian optimization framework integrates LEED multiple scattering models to jointly optimize structural and experimental parameters for automated surface reconstruction.
-
A tutorial on learning from preferences and choices with Gaussian Processes
Tutorial on a GP-based framework for preference and choice learning that unifies random utility models, limits of discernment, and multi-utility scenarios via customized likelihoods for object and label preferences.
-
Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.