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New SPaiK method enables scalable pairwise kernel learning

By PulseAugur Editorial · [2 sources] ·

Researchers have introduced SPaiK, a novel kernel learning method designed for pairwise settings that significantly reduces computational and memory demands. The core innovation is the stochastic generalized vec trick (sGVT), an extension of the sparse Kronecker product multiplication algorithm, which facilitates efficient large-scale training with pairwise kernels. This advancement allows kernel-based pairwise learning to be applied to previously unmanageable dataset sizes, as demonstrated by evaluations on seven drug-target affinity datasets. AI

IMPACT Enables larger-scale applications of kernel-based pairwise learning, particularly in domains like drug-target affinity prediction.

RANK_REASON The cluster contains a research paper published on arXiv detailing a new machine learning method.

Read on arXiv cs.LG →

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

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Napsu Karmitsa, Tapio Pahikkala, Antti Airola ·

    Scalable Pairwise Kernel Learning with Stochastic Vec Trick

    arXiv:2606.16979v1 Announce Type: new Abstract: Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel learning method tailored for pairwise settings. Our app…

  2. arXiv cs.LG TIER_1 English(EN) · Antti Airola ·

    Scalable Pairwise Kernel Learning with Stochastic Vec Trick

    Pairwise learning is a specialized form of supervised learning that focuses on predicting outcomes for pairs of objects. In this work, we introduce SPaiK, a new scalable kernel learning method tailored for pairwise settings. Our approach preserves the expressive power of kernel m…

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