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Found 7 papers, 1 papers with code
Combining additivity and active subspaces for high-dimensional Gaussian process modeling
Gaussian processes are a widely embraced technique for regression and classification due to their good prediction accuracy, analytical tractability and built-in capabilities for uncertainty quantification.
Shared active subspace for multivariate vector-valued functions
This paper proposes several approaches as baselines to compute a shared active subspace for multivariate vector-valued functions.
Trajectory-oriented optimization of stochastic epidemiological models
Epidemiological models must be calibrated to ground truth for downstream tasks such as producing forward projections or running what-if scenarios.
A portfolio approach to massively parallel Bayesian optimization
One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time.
Sequential Learning of Active Subspaces
In such cases, often a surrogate model is employed, on which finite differencing is performed.
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold.
A Bayesian optimization approach to find Nash equilibria
Game theory finds nowadays a broad range of applications in engineering and machine learning.
