Many modern unsupervised or semi-supervised machine learning algorithms rely on
Bayesian probabilistic models. These models are usually intractable and thus require …

We propose a new regularization method based on virtual adversarial loss: a new measure
of local smoothness of the conditional label distribution given input. Virtual adversarial loss …

Seismic tomography is a methodology to image the interior of solid or fluid media and is
often used to map properties in the subsurface of the Earth. In order to better interpret the …

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 …

Imitation learning uses data for training policies to solve complex tasks. However, when the
training data is collected from human demonstrators, it often leadsto multimodal distributions …

In a variety of scientific applications, we wish to characterize a physical system using
measurements or observations. This often requires us to solve an inverse problem, which …

Probability distributions are key components of many learning from demonstration (LfD)
approaches, with the spaces chosen to represent tasks playing a central role. Although the …

Black-box optimization is a versatile approach to solve complex problems where the
objective function is not explicitly known and no higher order information is available. Due to …

Haptic guidance is a powerful technique to combine the strengths of humans and
autonomous systems for teleoperation. The autonomous system can provide haptic cues to …

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 …