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.


RIKEN call for applications - Special Postdoctoral Researcher (SPDR) - open from Feb. 18 to Apr. 8, 2021.

You can apply to any team in RIKEN AIP. See here for details.

RIKEN's Programs for Junior Scientists

See here for details.

March 10, 2021

16th AIP Open Seminar: talks by Approximate Bayesian Inference Team. Talks by

  • Emtiyaz Khan: Bayesian principles for Learning-Machines,
  • Dharmesh Taylor: Memorable Experiences of Learning-Machines,
  • Pierre Alquier: Meta-Strategy for Hyperparameter Tuning with Guarantees.

Registration link – free but compulsory

January 28, 2021

Two papers were accepted at AISTATS2021:

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.

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