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Found 4 papers, 1 papers with code
Kernel computations from large-scale random features obtained by Optical Processing Units
Approximating kernel functions with random features (RFs)has been a successful application of random projections for nonparametric estimation.
Variational Calibration of Computer Models
Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters.
Efficient batch-sequential Bayesian optimization with moments of truncated Gaussian vectors
We deal with the efficient parallelization of Bayesian global optimization algorithms, and more specifically of those based on the expected improvement criterion and its variants.
Differentiating the multipoint Expected Improvement for optimal batch design
The computational burden of this selection rule being still an issue in application, we derive a closed-form expression for the gradient of the multipoint Expected Improvement, which aims at facilitating its maximization using gradient-based ascent algorithms.
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