Just wrapped up my Pure Exploration short course, it was an amazing experience 🚀 All the lectures are now online:

🔗 sites.google.com/vie...

Had a lot of fun teaching this, and I’d be happy to run it again for workshops, seminars, etc... just reach out! 📚

AD-RRL uses diffusion-guided adversarial trajectories to train robust policies using a CVaR objective.

It's a Dyna-style loop: collect rollouts; train a diffusion model; adversarially guide sampling to produce worst-case trajectories; train the RL agent on this data; iterate.

Excited to be in San Diego next week for #NeurIPS2025 🎉!

Will present Adversarial Diffusion for Robust RL together with @DanieleFoffano. Poster session on Fri 5 Dec 7:30 p.m. EST, Exhibit Hall C,D,E.

AD-RRL uses diffusion models to train Robust RL policies.

#RL #Diffusion

Honored to be named a Top Reviewer (10%) for #NeurIPS2025 🎉.

I’ll be in San Diego and happy to meet! I’ll also be with @DanieleFoffano presenting our latest on Robust RL with Diffusion Models arxiv.org/abs/2509.2.... Ping me if you want to chat on RL.

#RL#DiffusionModels

Excited for tomorrow, where Pure Exploration in RL kicks off at Boston University 🎉!

Lectures will be recorded & uploaded - stay tuned.
Many thanks to @bostonu.bsky.social (CDS) & @aldopacchiano.bsky.social for allowing this opportunity.
Website: sites.google.com/vie...

#BU #RL #PureExploration

80⭐️ on a tiny repo on data-driven control I put out during my PhD!

PyDeePC is a tiny Python implementation of Data-enabled Predictive Control. It is model-free, quick to try, easy to learn. Feedback welcome!

github.com/rssalessi...
#MPC #ControlTheory #DataDrivenControl

Thrilled to share that this work with Yichen Song and
@aldopacchiano.bsky.social got accepted for an oral presentation at AISTATS 2025!

Pure Exploration with Feedback Graphs arxiv.org/pdf/2503.07824

Code: github.com/rssalessio/P...

#AISTATS #AISTATS2025 #ML