RL for real-world applications = offline learning + reward learning. How do we make this work? Find out more at ICLR poster #377 at 10am today! @gioramponi.bsky.social and I will be presenting our latest work on offline preference-based RL (joint w/ @gxxxr.bsky.social and Bernhard Schölkopf).

🎉 Yesterday, @alizeepace.bsky.social, our first PhD Fellow of the @eth-ai-center.bsky.social, graduated! She was supervised by ETH AI Center Faculty members Prof. Rätsch @gxxxr.bsky.social and Prof. Schölkopf. Congrats, Dr. Pace! Next, she will join Google DeepMind in Zurich as a Research Scientist.

Last Friday, I had the pleasure of giving an invited talk at the workshop on Reinforcement Learning in Mannheim, Germany! I presented recent work on offline preference-based reinforcement learning, aiming to make RL more practical for real-world applications like healthcare.

Machine learning has made incredible breakthroughs, but our theoretical understanding lags behind. We take a step towards unravelling its mystery by explaining why the phenomenon of disentanglement arises in generative latent variable models. Blog post: carl-allen.github.io/theory/2024/...