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
Vacancies
Please have a look at open positions in our group. See here for more details and join our team.
October 03, 2025
Thomas, Hugo, and Christopher will present their works at the RIKEN AIP LLMxML Workshop.
September 19, 2025
Two papers accepted at NeurIPS 2025
- Variational Learning Finds Flatter Solutions at the Edge of Stability by Avrajit Ghosh et al. received a spotlight presentation (top 3%, 688 out 21575 submissions)
- Compact Memory for Continual Logistic Regression by Yohan Jung et al.
September 15, 2025
Emtiyaz Khan will be a keynote speaker at the 1st EurIPS conference (officially endorsed European version of NeurIPS).
September 01, 2025
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Guest Talk by Yingzhen Li: Variational Uncertainty Decomposition for In-Context Learning [doorkeeper] [Video]