Aaron Havens

New paper out with FAIR(+FAIR-Chemistry): Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching We present a scalable method for sampling from unnormalized densities beyond classical force fields. 📄: arxiv.org/abs/2504.11713

Adjoint-based diffusion samplers have simple & scalable objectives w/o impt weight complication. Like many, though, they solve degenerate Schrödinger bridges, despite all being SB-inspired. 📢 Proudly introduce #Adjoint #Schrödinger #Bridge #Sampler, a full SB-based sampler that…

Excited to share Quetzal, a simple but scalable model for building 3D molecules atom-by-atom. 🐉 Named after Quetzalcoatl, the Aztec god of creation We equip a standard causal transformer with a per-atom diffusion MLP to model the continuous 3D position of the next atom. [1/3]

Reward-driven algorithms for training dynamical generative models significantly lag behind their data-driven counterparts in terms of scalability. We aim to rectify this. Adjoint Matching poster @cdomingoenrich Sat 3pm & Adjoint Sampling oral @aaronjhavens Mon 10am FPI

We are presenting 3 orals and 1 spotlight at #ICLR2025 on two primary topics: On generalizing the data-driven flow matching algorithm to jump processes, arbitrary discrete corruption processes, and beyond. And on highly scalable algorithms for reward-driven learning settings.

Want to learn continuous & discrete Flow Matching? We've just released: 📙 A guide covering Flow Matching basics & advanced methods arxiv.org/abs/2412.06264. 💻 An open source codebase with image & text examples github.com/facebookresear…. 🗣️ A Flow Matching tutorial #NeurIPS2024.

Hi friends. I will be at the NYC FAIR office for the next 6 months as a research scientist intern. I’ll be working on all things control theory x generative modeling under the amazing @brandondamos. Please feel free to reach out if you’re around NYC!

A virtual session on the intersection of optimization, control and RL happening now (9am-12pm CT) for the annual CSL student conference! Find the schedule and zoom link here: studentconference.csl.illinois.edu/optimization-c…

Our review paper on **Safe Learning in Robotics** is out, including open-source code. Wondering how model-driven and data-driven approaches can work together to achieve safe learning? Check out the paper! arxiv.org/abs/2108.06266 @UofTRobotics @VectorInst @uoftengineering

I love all of you very much, so please don't beat me up for this.