arXiv: cs.AI preprintsInternational6 November 2026
Gradient Boosting for Efficient Rank Discovery in Tensor Network Kernel Machines
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arXiv:2611.08093v1 Announce Type: cross Abstract: Tensor network kernel machines (TNKMs) combine nonlinear features with low-rank tensor representations to model complex relationships efficiently. However, selecting an appropriate tensor rank remains challenging, as conventional rank-search methods require training multiple candidate models, increasing computational costs. In this work, we focus on a canonical polyadic (CP) tensor network parameterization and propose GB-CPKM, a gradient boosting framework that builds a higher-capacity predictor by sequentially adding fixed-rank learners. This
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