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
January 30, 2023
Our work “SAM as an Optimal Relaxation of Bayes” (T. Moellenhoff and M. E. Khan) is accepted at ICLR 2023 (notable top 5%). We have another paper “The Lie-Group Bayesian Learning Rule” (E. M. Kiral, T. Moellenhoff, M.E. Khan) accepted at AISTATS 2023.
January 20, 2023
Happy Buzaaba is co-organizing the ICLR2023 workshop on AfricaNLP to be held on 5th May in Kigali, Rwanda.
December 12, 2022
We organized the “Continual Lifelong Learning Workshop” at ACML2022 in Hyderabad, India.
December 08, 2022
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Invited Prof. Michael Choi (National University of Singapore and Yale-NUS College) for a seminar talk. Title “From reversiblizations of non-reversible Markov chains to landscape modification”.