Author

Yun-Hong Min

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#machine learning Preprint Oct 2026

Unbalanced W-Flow: Unbalanced Wasserstein Drifting under Finite-Batch Marginal Fluctuations

Teacher-free one-step generative models such as W-Flow learn directly from distributional objectives, with the Sinkhorn divergence providing the strongest empirical performance among the energies studied in W-Flow. However, Sinkhorn divergence is based on balanced optimal transport, whose exact marginal constraints can...

Jongwook Jeon, Mingue Park, Jiwon Choi et al. · 0 citations

#machine learning Preprint Aug 2026

I-SplineFlow: Learning Monotone Spline Stochastic Interpolant Schedulers for Few-Step Generation

I-SplineFlow is introduced, which parameterizes the scheduler with integrated monotone splines (I-splines), and improves few-step FID over B\'ezier scheduling in most settings, most clearly at the lowest NFEs, and trains in minutes.

Md Sakib Hossain Shovon, Md Rifat Ur Rahman, Md Abtahi Majeed Chowdhury et al. · 0 citations