99 citations · 347 across the 39 of their papers we have counts for
6 papers · 1 filter
Sample-efficient reinforcement learning using deep Gaussian processes
Charles Gadd, Markus Heinonen, Harri Lähdesmäki +1
Reinforcement learning provides a framework for learning to control which actions to take towards completing a task through trial-and-error. In many applications observing interact…
Scalable Bayesian neural networks by layer-wise input augmentation
Trung Trinh, Samuel Kaski, Markus Heinonen
We introduce implicit Bayesian neural networks, a simple and scalable approach for uncertainty representation in deep learning. Standard Bayesian approach to deep learning requires…
Bayesian Inference for Optimal Transport with Stochastic Cost
Anton Mallasto, Markus Heinonen, Samuel Kaski
In machine learning and computer vision, optimal transport has had significant success in learning generative models and defining metric distances between structured and stochastic…
Likelihood-Free Inference with Deep Gaussian Processes
Alexander Aushev, Henri Pesonen, Markus Heinonen +2
In recent years, surrogate models have been successfully used in likelihood-free inference to decrease the number of simulator evaluations. The current state-of-the-art performance…
Learning continuous-time PDEs from sparse data with graph neural networks
Valerii Iakovlev, Markus Heinonen, Harri Lähdesmäki
The behavior of many dynamical systems follow complex, yet still unknown partial differential equations (PDEs). While several machine learning methods have been proposed to learn P…
Sparse Gaussian Processes Revisited: Bayesian Approaches to Inducing-Variable Approximations
Simone Rossi, Markus Heinonen, Edwin V. Bonilla +2
Variational inference techniques based on inducing variables provide an elegant framework for scalable posterior estimation in Gaussian process (GP) models. Besides enabling scalab…