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

Talk at IG4DS - "Information Geometry of Reversible Markov Chains" [ Slides ]
Tutorial at SMILES 2020: Learning with Bayesian Principles.
Tutorial at SMILES 2020: Sequential Prediction Problems.

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's Programs for Junior Scientists

See here for details.

July 29, 2023

Thomas Moellenhoff and Emtiyaz Khan are organizing the Duality Principles for Modern ML Workshop at ICML 2023 in Hawaii.

July 21, 2023

From August 2 to August 9, Emtiyaz Khan, Gian Maria Marconi and Lu Xu will be giving invited talks at the Machine Learning Research School (MLRS), Bangkok, Thailand.

July 11, 2023

Emtiyaz Khan will present VB Made Easy at AABI 2023.

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