49 citations · 156 across the 29 of their papers we have counts for
5 papers · 1 filter
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,…
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning
Markus Kaiser, Clemens Otte, Thomas Runkler +1
In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learni…
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…
Invariant Feature Mappings for Generalizing Affordance Understanding Using Regularized Metric Learning
Martin Hjelm, Carl Henrik Ek, Renaud Detry +1
This paper presents an approach for learning invariant features for object affordance understanding. One of the major problems for a robotic agent acquiring a deeper understanding…