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

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.


February 02, 2022

P. Alquier invited speaker at the 4th Symposium on Advances in Approximate Bayesian Inference


January 20, 2022

P. Alquier gives a talk: “Matrix Factorization for Time Series Analysis” at the CDT in Modern Statistics and Statistical Machine Learning at Imperial and Oxford


December 08, 2021

Emtiyaz Khan, Dharmesh Tailor and Siddharth Swaroop will be giving an invited talk on Adaptive and Robust Learning with Bayes at the NeurIPS 2021 Bayesian deep learning workshop on Dec. 14th 11:10 - 11:30 GMT.


December 06, 2021

We won both tracks of the NeurIPS competition on Approximate Inference in Bayesian Deep Learning! Please join our talks at the NeurIPS 2021 competition track (Dec. 9th, 6pm GMT) and at the BDL workshop (Dec. 14th, 4:40pm GMT) to learn about our solution.


October 22, 2021

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


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