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New method tackles dynamic regret in RKHS using subspace approximation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new method for online regression in reproducing kernel Hilbert spaces (RKHS) that addresses dynamic regret. The approach adapts finite-dimensional techniques to the RKHS setting using subspace approximations. This method involves running an ensemble of discounted forecasters over various discount factors within a fixed subspace, with approximation errors managed by projection errors of kernel sections. AI

IMPACT Introduces a novel theoretical framework for dynamic regret in RKHS, potentially improving online learning algorithms.

RANK_REASON This is a research paper detailing a new theoretical method for online regression.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New method tackles dynamic regret in RKHS using subspace approximation

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This is a research paper detailing a new theoretical method for online regression.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dmitry B. Rokhlin, Georgiy A. Karapetyants ·

    Dynamic Regret for Online Regression in RKHS via Discounted VAW and Subspace Approximation

    arXiv:2604.25021v1 Announce Type: new Abstract: We study online regression with the square loss in a reproducing kernel Hilbert space under a dynamic regret criterion. The learner is compared with a time-varying comparator sequence, and the bounds depend on its path length in the…

  2. arXiv cs.LG TIER_1 English(EN) · Georgiy A. Karapetyants ·

    Dynamic Regret for Online Regression in RKHS via Discounted VAW and Subspace Approximation

    We study online regression with the square loss in a reproducing kernel Hilbert space under a dynamic regret criterion. The learner is compared with a time-varying comparator sequence, and the bounds depend on its path length in the RKHS norm. The proposed method transfers the fi…

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