The objective of this paper is to propose a novel deep learning methodology to gain
pragmatic insights into the behavior of an insured person using unsupervised variable …

A long-cherished vision in robotics is to equip robots with skills that match the versatility and
precision of humans. For example, when playing table tennis, a robot should be capable of …

Abstract Simulation-Based Inference (SBI) is a common name for an emerging family of
approaches that infer the model parameters when the likelihood is intractable. Existing SBI …

Variational inference with Gaussian mixture models (GMMs) enables learning of highly
tractable yet multi-modal approximations of intractable target distributions with up to a few …

Robots often rely on a repertoire of previously-learned motion policies for performing tasks
of diverse complexities. When facing unseen task conditions or when new task requirements …

Learning skills by imitation is a promising concept for the intuitive teaching of robots. A
common way to learn such skills is to learn a parametric model by maximizing the likelihood …

Approximate inference in Bayesian deep networks exhibits a dilemma of how to yield high
fidelity posterior approximations while maintaining computational efficiency and scalability …

Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-
based planning and for individual steps. Thus, they can easily generalize and adapt to new …

Computer models play a crucial role in numerous scientific and engineering domains. To
ensure the accuracy of simulations, it is essential to properly calibrate the input parameters …

An effective approach for sampling from unnormalized densities is based on the idea of
gradually transporting samples from an easy prior to the complicated target distribution. Two …