Happy to share one of my last works! If you are interested in diffusion samplers, please take a look🙃! Many thanks for all my colleagues for their intensive work and fruitful collaboration, especially for malkin1729 for leading this project! Stay tuned for the future ones!

My PhD thesis entitled "Generative Flow Networks: Theory and Applications to Structure Learning" is now available on Arxiv 🎓 📖 arxiv.org/abs/2501.05498 🔖 Want to learn what GFlowNets are? Check out Chapters 2, 3 & 4!

🚀 New Preprint! 🚀 In-Context Parametric Inference: Point or Distribution Estimators? Thrilled to share our work on inferring probabilistic model parameters explicitly conditioned on data, in collab with Yoshua Bengio, malkin1729 & Guillaume Lajoie! 🔗arxiv.org/abs/2502.11617

Ecstatic to show off some work my brilliant colleagues and I did at ICLR 2026 this year! 🚀 We address the credit assignment challenge under long trajectories in RL or GFlowNets by constructing high order actions, or “chunks”, effectively compressing trajectory lengths!

A great pleasure to crash two Bayesian statistics conferences with a dose of diffusion wisdom — last week in Singapore (bayescomp2025.sg), now in Cambridge (newton.ac.uk/event/rclw03/) — with the two authors of this very nice paper.

1/ 💻 Queer in AI is hosting a social at #ICML2025 in Vancouver on 📅 July 16, and you’re invited! Let’s network, enjoy food and drinks, and celebrate our community. Details below…

(1/n) The usual assumption in GFlowNet environments is acyclicity. Have you ever wondered if it can be relaxed? Does the existing GFlowNet theory translate to the non-acyclic case? Is efficient training possible? We shed new light on these questions in our latest work! ICML Conference

Ever the inhabiter of liminal spaces, I can also now be found fluttering in bluer skies 🦋

One of our three papers in “Frontiers in Probabilistic Inference” @ NeurIPS’25, along with arxiv.org/abs/2509.26364 and arxiv.org/abs/2510.01159. Pleasure to work with the brilliant tamogashev on all of them!