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.

**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**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.

**September 10, 2024**Thomas MÃ¶llenhoff will give invited talks:

- November 7, 2024 at the IBIS 2024 Machine Learning Theory workshop, Tokyo, Japan.
- December 4-5, 2024 at OCAMI Workshop, Osaka, Japan.

**August 28, 2024**Thomas MÃ¶llenhoff will give invited talks:

- September 12, 2024 at the Seminar on Advances in Probabilistic Machine Learning (Aalto University) and the Mathematical Optimization Research Seminar (Saarland University).
- September 25-27, 2024 at KAIST in Daejong, Korea.