Advances in Approximate Bayesian Inference

NIPS 2017 Workshop; December 8, 2017
Long Beach Convention Center, Long Beach, USA

Approximate inference is key to modern probabilistic modeling. Thanks to the availability of big data, significant computational power, and sophisticated models, machine learning has achieved many breakthroughs in multiple application domains. At the same time, approximate inference becomes critical since exact inference is intractable for most models of interest. Within the field of approximate Bayesian inference, variational and Monte Carlo methods are currently the mainstay techniques. For both methods, there has been considerable progress both on the efficiency and performance.

In this workshop, we encourage submissions advancing approximate inference methods. We are open to a broad scope of methods within the field of Bayesian inference. In addition, we also encourage applications of approximate inference in many domains, such as computational biology, recommender systems, differential privacy, and industry applications.

This workshop is a continuation of past years:

Invited Speakers and Panelists

Invited speakers (Academia)

Invited speakers (Industry)



Advisory Committee