Author

Minhyuk Sung

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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 Sep 2026

RW-Flow: One-Step Generation on Compact Manifolds via Wasserstein Gradient Flows

Manifold-valued data, and consequently the distributions they induce, are prevalent across many domains, ranging from the locations of geospatial events, such as earthquakes, to biomolecular torsion angles that encode information about three-dimensional structure. While diffusion and flow-based generative models have b...

Ualibyek Nurgulan, Seungwoo Yoo, Prin Phunyaphibarn 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

Preprint Jul 2026

Tensor-Train Joint Modeling for Few-Step Discrete Diffusion

The first framework for explicit joint distribution modeling in discrete diffusion via tensor decomposition, which represents the conditional clean distribution as a low-rank tensor with controllable expressivity, is presented.

Byoungkwon Kim, Minhyuk Sung · 0 citations