184 citations · 540 across the 36 of their papers we have counts for
6 papers · 1 filter
Expressive Mortality Models through Gaussian Process Kernels
Mike Ludkovski, Jimmy Risk
We develop a flexible Gaussian Process (GP) framework for learning the covariance structure of Age- and Year-specific mortality surfaces. Utilizing the additive and multiplicative…
Adaptive Batching for Gaussian Process Surrogates with Application in Noisy Level Set Estimation
Xiong Lyu, Mike Ludkovski
We develop adaptive replicated designs for Gaussian process metamodels of stochastic experiments. Adaptive batching is a natural extension of sequential design heuristics with the…
Evaluating Gaussian Process Metamodels and Sequential Designs for Noisy Level Set Estimation
Xiong Lyu, Mickael Binois, Michael Ludkovski
We consider the problem of learning the level set for which a noisy black-box function exceeds a given threshold. To efficiently reconstruct the level set, we investigate Gaussian…
Probabilistic Bisection with Spatial Metamodels
Sergio Rodriguez, Mike Ludkovski
Probabilistic Bisection Algorithm performs root finding based on knowledge acquired from noisy oracle responses. We consider the generalized PBA setting (G-PBA) where the statistic…
Generalized Probabilistic Bisection for Stochastic Root-Finding
Sergio Rodriguez, Michael Ludkovski
We consider numerical schemes for root finding of noisy responses through generalizing the Probabilistic Bisection Algorithm (PBA) to the more practical context where the sampling…
Sequential Design for Ranking Response Surfaces
Ruimeng Hu, Mike Ludkovski
We propose and analyze sequential design methods for the problem of ranking several response surfaces. Namely, given response surfaces over a continuous input space $\cal…