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Found 8 papers, 3 papers with code
High-dimensional additive Gaussian processes under monotonicity constraints
First, we show that our framework enables to satisfy the constraints everywhere in the input space.
A comparison of mixed-variables Bayesian optimization approaches
In this article, costly mixed problems are approached through Gaussian processes where the discrete variables are relaxed into continuous latent variables.
Approximating Gaussian Process Emulators with Linear Inequality Constraints and Noisy Observations via MC and MCMC
Finally, on 2D and 5D coastal flooding applications, we show that more flexible and realistic GP implementations can be obtained by considering noise effects and by enforcing the (linear) inequality constraints.
Finite-dimensional Gaussian approximation with linear inequality constraints
Introducing inequality constraints in Gaussian process (GP) models can lead to more realistic uncertainties in learning a great variety of real-world problems.
On the choice of the low-dimensional domain for global optimization via random embeddings
Then, the search of solutions can be reduced to the random embedding of a low dimensional space into the original one, resulting in a more manageable optimization problem.
Poincaré inequalities on intervals -- application to sensitivity analysis
We give semi-analytical results for some frequent distributions (truncated exponential, triangular, truncated normal), and present a numerical method in the general case.
A warped kernel improving robustness in Bayesian optimization via random embeddings
This works extends the Random Embedding Bayesian Optimization approach by integrating a warping of the high dimensional subspace within the covariance kernel.
Invariances of random fields paths, with applications in Gaussian Process Regression
We study pathwise invariances of centred random fields that can be controlled through the covariance.
