99 citations · 347 across the 39 of their papers we have counts for
5 papers · 1 filter
De-randomizing MCMC dynamics with the diffusion Stein operator
Zheyang Shen, Markus Heinonen, Samuel Kaski
Approximate Bayesian inference estimates descriptors of an intractable target distribution - in essence, an optimization problem within a family of distributions. For example, Lang…
Evolving-Graph Gaussian Processes
David Blanco-Mulero, Markus Heinonen, Ville Kyrki
Graph Gaussian Processes (GGPs) provide a data-efficient solution on graph structured domains. Existing approaches have focused on static structures, whereas many real graph data r…
Variational multiple shooting for Bayesian ODEs with Gaussian processes
Pashupati Hegde, Çağatay Yıldız, Harri Lähdesmäki +2
Recent machine learning advances have proposed black-box estimation of unknown continuous-time system dynamics directly from data. However, earlier works are based on approximative…
Affine Transport for Sim-to-Real Domain Adaptation
Anton Mallasto, Karol Arndt, Markus Heinonen +2
Sample-efficient domain adaptation is an open problem in robotics. In this paper, we present affine transport -- a variant of optimal transport, which models the mapping between st…
Continuous-Time Model-Based Reinforcement Learning
Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki
Model-based reinforcement learning (MBRL) approaches rely on discrete-time state transition models whereas physical systems and the vast majority of control tasks operate in contin…