Generative Diffusion Models (GDMs) have emerged as a transformative force in the realm of
Generative Artificial Intelligence (GenAI), demonstrating their versatility and efficacy across …

Diffusion models, a powerful and universal generative artificial intelligence technology, have
achieved tremendous success and opened up new possibilities in diverse applications. In …

Given an unconditional diffusion model and a predictor for a target property of interest (eg, a
classifier), the goal of training-free guidance is to generate samples with desirable target …

One important property of DIstribution Correction Estimation (DICE) methods is that the
solution is the optimal stationary distribution ratio between the optimized and data collection …

Diffusion models have emerged as effective distribution estimators in vision, language, and
reinforcement learning, but their use as priors in downstream tasks poses an intractable …

Score-based generative models like the diffusion model have been testified to be effective in
modeling multi-modal data from image generation to reinforcement learning (RL). However …

User intentions are typically formalized as evaluation rewards to be maximized when fine-
tuning language models (LMs). Existing alignment methods, such as Direct Preference …

Safe offline RL is a promising way to bypass risky online interactions towards safe policy
learning. Most existing methods only enforce soft constraints, ie, constraining safety …

Diffusion models excel at modeling complex and multimodal trajectory distributions for
decision-making and control. Reward-gradient guided denoising has been recently …

Diffusion-based generative methods have proven effective in modeling trajectories with
offline datasets. However, they often face computational challenges and can falter in …