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How Discrete and Continuous Diffusion Meet: Comprehensive Analysis of Discrete Diffusion Models via a Stochastic Integral Framework ift.tt/McHLghU

Unfortunately I have to miss NeurIPS this year (a very last minute decision…) However, my awesome collaborator Yinuo Ren will be there to present our spotlight poster on the theoretical foundation of accelerating diffusion models with parallel sampling - please check it out!!!

Check out our *spotlight* poster at East Exhibit Hall #2411! #NeurIPS2024

Speculative sampling accelerates inference in LLMs by drafting future tokens which are verified in parallel. With Valentin De Bortoli , Alexandre Galashov & Arthur Gretton, we extend this approach to (continuous-space) diffusion models: arxiv.org/abs/2501.05370

Glad to share that this paper has been accepted to #ICLR2025 ! Thank again my great collaborator Haoxuan (Steve) Chen, @GrantRotskoff, & Lexing Ying . We will continue to unravel and elucidate the mathematical foundations of diffusion-based generative models. Check out the paper

Great talk by Grant Rotskoff linking sampling with nonequilibrium physics in the #FPIworkshop. Come by for the poster session (Peridot 202-203) for the next hour. #ICLR25

arxiv.org/abs/2506.03979

Explore our work on posterior sampling with diffusion prior: ✅Training-free ✅Approximation-free ✅SOTA performance Please check out the full paper here: arxiv.org/abs/2506.03979

🚀 New Paper at #icml2025🚀 Diffusion models have succeeded in handling data of various modalities, such as 🖼️images (continuous), 💬languages (discrete), and 🌐manifolds (Riemannian), etc. 🤔 But how about data of 𝐦𝐢𝐱𝐞𝐝-𝐦𝐨𝐝𝐚𝐥𝐢𝐭𝐲? We investigate this question in