Variational inference with Gaussian mixture models (GMMs) can be used to learn highly tractable approximations of ...

Many problems in machine learning involve inference from intractable distributions. For example, when learning latent ...

In variational inference, we want to approximate an intractable target distribution (often given as a posterior ...

Reinforcement Learning is a powerful approach to achieve optimal behaviour. However, it typically requires a manual ...

Precise models of the system dynamics are crucial for model-based control and reinforcement learning (RL) in autonomous ...

Recurrent State-space models (RSSMs) are highly expressive models for learning patterns in time series data and system ...

Stochastic-search algorithms are problem independent algorithms well-suited for black-box optimization of an objective ...