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.
Please have a look at open positions in our group. See here for more details and join our team.
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].
New preprint avilable: Model Merging by Uncertainty-Based Gradient Matching.
One paper accepted at TMLR: Improving Continual Learning by Accurate Gradient Reconstructions of the Past.
Emtiyaz Khan is co-organizing a Dagstuhl Seminar On the Role of Bayesianism in the Age of Modern AI. It will be held from Nov. 10 - Nov. 15, 2024.