Chenyang Yuan

4/4 The model I used was GPT 5.4 xhigh on Codex. The code, prompts, harness, verification scripts, generated proof blueprint and Lean formalization are in this GitHub repo: github.com/yuanchenyang/n…

3/4 The harness gave the agent a computational toolkit to autonomously search for counterexamples using optimization solvers, so it can infer structure from dual certificates, write a blueprint, formalize the proof, and keep going until Lean accepted the final theorem.

I wrote a blog post about a Codex harness/workflow I built to autonomously prove a new mathematical result after 3 days of continuous work producing ~60k lines of Lean, where the input is a Lean theorem statement and output is a fully formalized proof. 1/4 chenyang.co/blog/automatic…

I'm helping to organize this CVPR tutorial on analytic understanding of diffusion models, join us if you are interested in learning more about how diffusion models generalize!

Why do diffusion models produce new images instead of just memorizing the dataset? We show that they learn pixel correlation patterns from the data and therefore denoise locally, which promotes generalization. To test this idea, we compare trained diffusion models with a training-free algorithm that mixes local patches from the dataset. Surprisingly, this simple procedure already reproduces many properties of the trained models. 🧵 Check out this thread for more details about our Spotlight NeurIPS paper with @yuancy, @JustinMSolomon and @vincesitzmann.

At #NeurIPS today in San Diego? Come check out poster #4409 (4:30–7:30 PM) today. We’re excited to share our spotlight paper on the generalization properties of diffusion models. Looking forward to great research conversations! @yuancy @JustinMSolomon @vincesitzmann

I played a small part in the production of this video, and I'm really happy with how it addressed common misconceptions about diffusion models, as well as the beautiful visualizations and animations!

@alec_helbling Thanks, I had a lot of fun building it!

This smalldiffusion project is really fun. It is a tiny library for training and sampling from diffusion models. I love efforts like this, they are much easier to play around with than big libraries like diffusers. Link: github.com/yuanchenyang/s…

In the problem sets, we use the library introduced in the first lecture (github.com/yuanchenyang/s…) to train diffusion models on custom data, as well as using pretrained models as building blocks for a variety of downstream tasks (see examples above) (4/4)

Using score distillation for 3D shape generation (L5) and wrapping up with a summary of the latest research making diffusion models better and faster (L6) The lecture recordings can be found here: youtube.com/playlist?list=… (3/4)

Last month I cotaught a class on diffusion models at MIT during the IAP term: practical-diffusion.org In the lectures, we first introduced diffusion models from a practitioner's perspective, showing how to build a simple but powerful implementation from the ground up (L1) (1/4)