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D2PO: Optimizing Diffusion Samplers via Dynamic Preference
Jinkyu Kim, Jinyoung Choi, Bohyung Han
We propose D2PO (Dynamic Direct Preference Optimization), a principled framework for optimizing diffusion sampling policies with respect to timestep schedules and classifier-free g…
Probabilistic Signature Inversion: Learning Conditional Distributions from Truncated Signatures
Junoh Kang, Kiseop Lee, Bohyung Han
The signature transform is a principled feature map for continuous-time paths, valued for its uniqueness and universality. Recovering a path from its truncated signature is, howeve…
Score-Repellent Monte Carlo: Toward Efficient Non-Markovian Sampler with Constant Memory in General State Spaces
Jie Hu, Lingyun Chen, Geeho Kim +3
History-dependent sampling can reduce long-run Monte Carlo variance by discouraging redundant revisits, but existing schemes typically encode history through empirical measure on f…
Enhanced Diffusion Sampling via Extrapolation with Multiple ODE Solutions
Jinyoung Choi, Junoh Kang, Bohyung Han
Diffusion probabilistic models (DPMs), while effective in generating high-quality samples, often suffer from high computational costs due to their iterative sampling process. To ad…
Observation-Guided Diffusion Probabilistic Models
Junoh Kang, Jinyoung Choi, Sungik Choi +1
We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM), which effectively addresses the tradeoff between qual…