We survey latent variable models for solving data-analysis problems. A latent variable model
is a probabilistic model that encodes hidden patterns in the data. We uncover these patterns …

Probabilistic modeling is iterative. A scientist posits a simple model, fits it to her data, refines
it according to her analysis, and repeats. However, fitting complex models to large data is a …

Inverse graphics attempts to take sensor data and infer 3D geometry, illumination, materials,
and motions such that a graphics renderer could realistically reproduce the observed scene …

Several decades of research in the field of machine learning have resulted in a multitude of
different algorithms for solving a broad range of problems. To tackle a new application, a …

Software tools for Bayesian inference have undergone rapid evolution in the past three
decades, following popularisation of the first generation MCMC-sampler implementations …

Modelling is fundamental to many fields of science and engineering. A model can be
thought of as a representation of possible data one could predict from a system. The …

We introduce a new regression framework, Gaussian process regression networks (GPRN),
which combines the structural properties of Bayesian neural networks with the non …

As global warming soars, the need to assess the environmental impact of research is
becoming increasingly urgent. Despite this, few recommender systems research papers …

Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …

Probabilistic programming systems and machine learning frameworks like Pyro, PyMC3,
TensorFlow, and PyTorch provide scalable and efficient primitives for inference and training …