69 citations · 1.1k across the 120 of their papers we have counts for
4 papers · 2 filters
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…
Informative Bayesian Neural Network Priors for Weak Signals
Tianyu Cui, Aki Havulinna, Pekka Marttinen +1
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.…
Projective Preferential Bayesian Optimization
Petrus Mikkola, Milica Todorović, Jari Järvi +2
Bayesian optimization is an effective method for finding extrema of a black-box function. We propose a new type of Bayesian optimization for learning user preferences in high-dimen…