Henry Li

About Me: I study the theory, guidance and fine-tuning of diffusion models. I have previously interned with the Seed Foundation Team at TikTok / ByteDance, the Center for Artificial Intelligence at Bosch, and the Center for Computational Mathematics at the Flatiron Institute. I have published both theoretical and applied work on diffusion models.
Theoretical
- ICML 2023 Workshop 1) Exponential weight averaging as damped harmonic motion 2) Non-normal Diffusion Models : Non-normal diffusion processes and connecting EMA training of diffusion models to two-body Hookean systems.
- ICLR 2024 Spotlight Likelihood Training of Cascaded Diffusion Models via Hierarchical Volume-preserving Maps
: Retaining likelihood estimation capabilities in multi-stage diffusion models (in collaboration with Meta AI).
Applied
- NeurIPS 2024a Solving Inverse Problems via Diffusion Optimal Control
: Guided diffusion modeling through the lens of optimal control (in collaboration with Bosch AI). - NeurIPS 2024b Boosting Alignment for Post-Unlearning Text-to-Image Generative Models
: Model alignment and unlearning in diffusion models. - In Submission Measurement Consistent Tweedie’s Solving Inverse Problems with the Conditional Posterior Mean (see "publications" for pre-print) : Fast and efficient guided diffusion modeling for image reconstruction in the presence of measurement noise.
- In Submission Dual Diffusion for Unified Image Generation and Understanding
: Large-scale diffusion models for simultaneous text and image generation (in collaboration with Seed Team at TikTok / ByteDance).
news
Feb 01, 2025 | On the market for research positions in generative modeling. If you know of a suitable role, please reach out! |
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Dec 01, 2024 | Two papers accepted to NeurIPS 2024! |
Jul 01, 2024 | One spotlight paper accepted to ICLR 2024! |