Our new work arxiv.org/pdf/2503.01006 extends the theory of diffusion bridges to degenerate noise settings, including underdamped Langevin dynamics (with Denis Blessing, Julius Berner). This enables more efficient diffusion-based sampling with substantially fewer discretization steps.

I am happy to share that our work ‘DIME: Diffusion-Based Maximum Entropy Reinforcement Learning’ has been accepted to ICML 2025. Many thanks to my colleagues and collaborators Zechu Li, Denis Blessing, Ge Li, Daniel Palenicek, Jan Peters, Georgia Chalvatzaki,Gerhard Neumann

1/ Where do Probabilistic Models, Sampling, Deep Learning, and Natural Sciences meet? 🤔 The workshop we’re organizing at #NeurIPS2025! 📢 FPI@NeurIPS 2025: Frontiers in Probabilistic Inference – Learning meets Sampling Learn more and submit → fpiworkshop.org

Excited for #ICML2025 in Vancouver! On Thursday morning, I'm presenting our paper (arxiv.org/pdf/2506.00962) on a critical issue in reinforcement learning: how to correctly handle random time horizons. We've identified incorrect formulas and offer a solution. Let's chat, write me!

When sampling from multimodal distributions, we rely on multiple temperatures to balance exploration and exploitation. Can we bring this idea into the world of diffusion-based neural samplers? 👉Check out our ICML paper to see how this idea can lead to significant improvements!