8 papers
Function-Space Diffusion for Motion Planning
Zinuo Chang, Yipu Chen, Byoungwoo Park +2
Diffusion-based motion planners have demonstrated strong performance in generating diverse and high-quality robot trajectories in cluttered environments with multiple feasible solu…
Coarse-to-Fine Compositional Diffusion for Long-Horizon Planning
Byoungwoo Park, Utkarsh A. Mishra, Jaemoo Choi +2
Diffusion models provide strong priors for generating structured data, but many tasks require outputs beyond the scale on which these models are typically trained. Compositional ge…
Improving Constrained Language Generation via Self-Distilled Twisted Sequential Monte Carlo
Sooyeon Kim, Giung Nam, Byoungwoo Park +1
Recent work has framed constrained text generation with autoregressive language models as a probabilistic inference problem. Among these, Zhao et al. (2024) introduced a promising…
Functional Adjoint Sampler: Scalable Sampling on Infinite Dimensional Spaces
Byoungwoo Park, Juho Lee, Guan-Horng Liu
Learning-based methods for sampling from the Gibbs distribution in finite-dimensional spaces have progressed quickly, yet theory and algorithmic design for infinite-dimensional fun…
Multi-Marginal Schrödinger Bridge Matching
Byoungwoo Park, Juho Lee
Understanding the continuous evolution of populations from discrete temporal snapshots is a critical research challenge, particularly in fields like developmental biology and syste…
Amortized Control of Continuous State Space Feynman-Kac Model for Irregular Time Series
Byoungwoo Park, Hyungi Lee, Juho Lee
Many real-world datasets, such as healthcare, climate, and economics, are often collected as irregular time series, which poses challenges for accurate modeling. In this paper, we…