A Hilbert space embedding of a distribution—in short, a kernel mean embedding—has
recently emerged as a powerful tool for machine learning and statistical inference. The basic …

Despite many debates in the first half of the twentieth century, it is now largely a truism that
humans and other animals build models of their environments and use them for prediction …

Bayesian methods for machine learning have been widely investigated, yielding principled
methods for incorporating prior information into inference algorithms. In this survey, we …

Autonomous learning has been a promising direction in control and robotics for more than a
decade since data-driven learning allows to reduce the amount of engineering knowledge …

In this paper, we introduce pilco, a practical, data-efficient model-based policy search
method. Pilco reduces model bias, one of the key problems of model-based reinforcement …

Novelty and surprise play significant roles in animal behavior and in attempts to understand
the neural mechanisms underlying it. They also play important roles in technology, where …

From household appliances to applications in robotics, engineered systems involving
complex dynamics can only be as effective as the algorithms that control them. While …

A broad range of neural and behavioral data suggests that the brain contains multiple
systems for behavioral choice, including one associated with prefrontal cortex and another …

We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series
analysis, with applications to learning models of human pose and motion from high …

We propose Bayesian Deep Q-Network (BDQN), a practical Thompson sampling based
Reinforcement Learning (RL) Algorithm. Thompson sampling allows for targeted exploration …