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.
December 12, 2024
New preprint on improving multitask finetuning using Bayesian model merging is available on arXiv.
December 06, 2024
Our team have two accepted workshops at ICLR 2025: “Quantify Uncertainty and Hallucination” and “XAI4Science”.
November 15, 2024
Emtiyaz Khan gives a Distinguished Lecture at CISPA Helmholtz Center for Information Security.
October 15, 2024
The 2nd Bayes-duality workshop videos are now publicly available.
- Playlist: https://bayesduality.github.io/workshop_2024.html#videos
- Each talk separately: https://bayesduality.github.io/talks_2024.html
September 19, 2024
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Our team has two open positions for research scientist or post-doc. The candidate will work on problems at the intersection of deep learning, Bayesian inference, optimization and reinforcement learning. Please check here for more details.