Welcome to the webpage of the Approximate Bayesian Inference Team @ RIKEN AIP.


Humans, animals, and other living beings have a natural ability to autonomously learn throughout their lives and quickly adapt to their surroundings, but computers lack such abilities. Our goal is to bridge such gaps between the learning of living-beings and computers. We are machine learning researchers with an expertise in areas such as approximate inference, Bayesian statistics, continuous optimization, information geometry, etc. We work on a variety of learning problems, especially those involving supervised, continual, active, federated, online, and reinforcement learning. Please check out research and publications pages for a more exhaustive overview.

If you are interested in joining us, see the people page and the news below for current opportunities.

News

Open Position

One post-doc/technical staff position available. For further inquiries e-mail at: jobs-abi-riken-aip@googlegroups.com. Emails sent to other addresses will not be answered.


RIKEN's Programs for Junior Scientists

See here for details.


November 11, 2020

“Approximate Bayesian Inference”, the editorial of a forthcoming special issue in Entropy, written by P. Alquier, is now published: paper. You can submit a paper until the end of Feb. 2021. Special Issue on Approximate Bayesian Inference


November 05, 2020

The videos of two recent talks by Pierre Alquier are now online:


November 02, 2020

A series of seminars on “Bayesian principles for learning machines” held by Emtiyaz Khan will take place at the following dates and locations.


October 06, 2020

Pierre Alquier joined the editorial board of JMLR.


September 26, 2020

Our paper on continual learning by functional regularization on the memorable past is accepted as an oral presentation at NeurIPS 2020.


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