Unsupervised learning of probabilistic models is a central yet challenging problem in
machine learning. Specifically, designing models with tractable learning, sampling …

Gaussian state space models have been used for decades as generative models of
sequential data. They admit an intuitive probabilistic interpretation, have a simple functional …

We introduce Deep Variational Bayes Filters (DVBF), a new method for unsupervised
learning and identification of latent Markovian state space models. Leveraging recent …

Kalman Filters are one of the most influential models of time-varying phenomena. They
admit an intuitive probabilistic interpretation, have a simple functional form, and enjoy …

The Bayesian framework for machine learning allows for the incorporation of prior
knowledge in a coherent way, avoids overfitting problems, and provides a principled basis …

We develop a nonlinear generalization of independent component analysis (ICA) or blind
source separation, based on temporal dependencies (eg autocorrelations). We introduce a …

Abstract Machine learning has seen a significant surge and uptake across many diverse
applications. The high flexibility, adaptability, and computing capabilities it provides extend …

State-space models have been successfully used for more than fifty years in different areas
of science and engineering. We present a procedure for efficient variational Bayesian …

This book examines Gaussian processes in both model-based reinforcement learning (RL)
and inference in nonlinear dynamic systems. First, we introduce PILCO, a fully Bayesian …

This paper presents a variational treatment of dynamic models that furnishes time-
dependent conditional densities on the path or trajectory of a system's states and the time …