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New research explores nonparametric regression in reproducing kernel Hilbert spaces

By PulseAugur Editorial · [3 sources] ·

Two new research papers explore advanced nonparametric regression techniques within reproducing kernel Hilbert spaces. The first paper details a comprehensive theory for regularized M-estimation, establishing existence and measurability for various loss functions and proving sharp convergence rates. The second paper introduces a subsampling scheme for supervised learning in these spaces, aiming to reduce computational costs while maintaining accuracy, and demonstrates its practicability through numerical studies. AI

IMPACT These papers advance theoretical understanding in machine learning, potentially leading to more efficient and accurate algorithms for complex data analysis.

RANK_REASON Two academic papers published on arXiv detailing theoretical advancements in machine learning.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New research explores nonparametric regression in reproducing kernel Hilbert spaces

COVERAGE [3]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence

    We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of the estimator, covering a wide range of convex and non-convex losses, including bounded robust losses…

  2. arXiv stat.ML TIER_1 English(EN) · Ioannis Kalogridis ·

    Generalized nonparametric regression in reproducing kernel Hilbert spaces: Consistency and rates of convergence

    We develop a comprehensive theory for regularized M-estimation in reproducing kernel Hilbert spaces. Under mild conditions on the loss we establish existence and measurability of the estimator, covering a wide range of convex and non-convex losses, including bounded robust losses…

  3. arXiv stat.ML TIER_1 English(EN) · Maxime Sangnier ·

    Subsampling for supervised learning in reproducing kernel Hilbert spaces

    In the era of big data, subsampling became a common practice in statistical learning. By selecting a subgroup of individuals based on which the learner is trained, subsampling aims at reducing the computational cost and time of the estimation step, and ideally leads to a decrease…

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