4 citations · 9 across the 6 of their papers we have counts for
12 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…
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…
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…
ODEVAE: Deep generative second order ODEs with Bayesian neural networks
Çağatay Yıldız, Markus Heinonen, Harri Lähdesmäki
We present Ordinary Differential Equation Variational Auto-Encoder (ODEVAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in de…
Learning spectrograms with convolutional spectral kernels
Zheyang Shen, Markus Heinonen, Samuel Kaski
We introduce the convolutional spectral kernel (CSK), a novel family of non-stationary, nonparametric covariance kernels for Gaussian process (GP) models, derived from the convolut…