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Jisu Kim

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Preprint Oct 2026

Semi-Supervised Kernel Ridge Fr\'echet Regression

Fr\'echet regression provides a framework for predicting random object-valued responses by minimizing the conditional expected squared distances. We propose semi-supervised kernel ridge Fr\'echet regression (SS-KRFR) to exploit unlabeled covariates when labeled samples are scarce. The method learns kernel spectral feat...

Jae Won Seo, Kwangho Kim, Jisu Kim · 0 citations
#machine learning Preprint Sep 2026

Nonparametric In-Context Learning under Growing Geometric Complexity: Minimax Optimality and Local Geometry-Adaptivity of Transformers

These results identify conditions under which the resulting predictor exploits local geometry and attains the aggregate minimax rate, and derive an in-context generalization bound for near empirical risk minimizers over this class.

J. Seo, Jisu Kim · 0 citations
#machine learning Preprint Sep 2026

Minimax Lower Bound for Estimating Diffusion-based Local Intrinsic Dimension

While diffusion-based methods have recently emerged as effective tools for probing the intrinsic geometry of high-dimensional data, their statistical difficulty remains largely unexplored. We study estimation of the finite-scale population functional underlying FLIPD (Kamkari et al., 2024; arXiv:2406.03537), a diffusio...

J. Seo, Won Yeon Jeong, Jisu Kim · 0 citations

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