49 citations · 156 across the 29 of their papers we have counts for
3 papers · 2 filters
Compositional uncertainty in deep Gaussian processes
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Markus Kaiser +3
Gaussian processes (GPs) are nonparametric priors over functions. Fitting a GP implies computing a posterior distribution of functions consistent with the observed data. Similarly,…
Modulating Surrogates for Bayesian Optimization
Erik Bodin, Markus Kaiser, Ieva Kazlauskaite +3
Bayesian optimization (BO) methods often rely on the assumption that the objective function is well-behaved, but in practice, this is seldom true for real-world objectives even if…
Monotonic Gaussian Process Flow
Ivan Ustyuzhaninov, Ieva Kazlauskaite, Carl Henrik Ek +1
We propose a new framework for imposing monotonicity constraints in a Bayesian nonparametric setting based on numerical solutions of stochastic differential equations. We derive a…