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.
February 27, 2024
“Geometric Aspects of Data-Processing of Markov Chains” (G. Wolfer, S. Watanabe) is accepted for publication in Transactions of Mathematics and Its Applications.
January 17, 2024
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We have two papers accepted to ICLR 2024:
- N. Daheim, T. Möllenhoff, E. M. Ponti, I. Gurevych, M. E. Khan, [Model Merging by Uncertainty-Based Gradient Matching]
- E. Guha, S. Natarajan, T. Möllenhoff, M. E. Khan, E. Ndiaye, [Conformal Prediction via Regression-as-Classification].