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.
See here for details.
“Empirical and Instance-Dependent Estimation of Markov Chain and Mixing Time” (G. Wolfer) is accepted for publication in Scandinavian Journal of Statistics.
“Systematic Approaches to Generate Reversiblizations of Markov Chains” (M. C.H. Choi and G. Wolfer) is accepted for publication in IEEE Transactions on Information Theory.
Thomas Moellenhoff and Emtiyaz Khan are organizing the Duality Principles for Modern ML Workshop at ICML 2023 in Hawaii.
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.