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.


Up to 4 Open Position (Research Scientist/Postdoctoral Researcher/Technical Staff)

See here for details.

RIKEN's Programs for Junior Scientists

See here for details.

October 22, 2021

On arXiv today: User-friendly introduction to PAC-Bayes bounds by P. Alquier

October 01, 2021

Our Bayes-duality project is launched with a funding of $2.76 million by JST-ANR’s CREST proposal

September 30, 2021

Invited talk by Pierre Alquier at AmLab (Amsterdam) on [MMD based estimation]

September 29, 2021

Two papers accepted at NeurIPS 2021:

September 28, 2021

Paper by Dimitri Meunier & Pierre Alquier accepted+published in Entropy: [Meta-strategy for Learning Tuning Parameters with Guarantees]

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