50 citations · 57 across the 5 of their papers we have counts for
5 papers
Error Bounds for a Kernel-Based Constrained Optimal Smoothing Approximation
Laurence Grammont, François Bachoc, Andrés F. López-Lopera
This paper establishes error bounds for the convergence of a piecewise linear approximation of the constrained optimal smoothing problem posed in a reproducing kernel Hilbert space…
High-dimensional additive Gaussian processes under monotonicity constraints
Andrés F. López-Lopera, François Bachoc, Olivier Roustant
We introduce an additive Gaussian process framework accounting for monotonicity constraints and scalable to high dimensions. Our contributions are threefold. First, we show that ou…
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande +3
Adding inequality constraints (e.g. boundedness, monotonicity, convexity) into Gaussian processes (GPs) can lead to more realistic stochastic emulators. Due to the truncated Gaussi…
Finite-dimensional Gaussian approximation with linear inequality constraints
Andrés F. López-Lopera, François Bachoc, Nicolas Durrande +1
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems. We consider the fini…
Nested Kriging predictions for datasets with large number of observations
Didier Rullière, Nicolas Durrande, François Bachoc +1
This work falls within the context of predicting the value of a real function at some input locations given a limited number of observations of this function. The Kriging interpola…