2 citations · 3 across the 11 of their papers we have counts for
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Beyond Pairwise Preferences: Listwise Reward-Aware Alignment for Diffusion Models
Austin Wang, Jiaqi Han, Stefano Ermon +1
Preference optimization has emerged as an efficient alternative to online reinforcement learning from human feedback (RLHF) for aligning text-to-image diffusion models. However, ex…
Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors
Hongkai Zheng, Austin Wang, Zihui Wu +3
Derivative-free Bayesian inversion arises in science and engineering applications, particularly when forward model is costly or infeasible to differentiate through. Existing deriva…
Discrete Diffusion Trajectory Alignment via Stepwise Decomposition
Jiaqi Han, Austin Wang, Minkai Xu +6
Discrete diffusion models have demonstrated great promise in modeling various sequence data, ranging from human language to biological sequences. Inspired by the success of RL in l…
InverseBench: Benchmarking Plug-and-Play Diffusion Priors for Inverse Problems in Physical Sciences
Hongkai Zheng, Wenda Chu, Bingliang Zhang +9
Plug-and-play diffusion priors (PnPDP) have emerged as a promising research direction for solving inverse problems. However, current studies primarily focus on natural image restor…
Split Gibbs Discrete Diffusion Posterior Sampling
Wenda Chu, Zihui Wu, Yifan Chen +2
We study the problem of posterior sampling in discrete-state spaces using discrete diffusion models. While posterior sampling methods for continuous diffusion models have achieved…
Ensemble Kalman Diffusion Guidance: A Derivative-free Method for Inverse Problems
Hongkai Zheng, Wenda Chu, Austin Wang +3
When solving inverse problems, one increasingly popular approach is to use pre-trained diffusion models as plug-and-play priors. This framework can accommodate different forward mo…