Spectral algorithms for supervised learning
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New spectral algorithms accelerate Markov chain convergence
Researchers have developed spectral algorithms for selecting state-space partitions that define averaging kernels for finite Markov chains. These algorithms aim to accelerate convergence by composing or mixing a baselin…
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New research bounds spectral ranking errors against adaptive adversaries
Researchers have analyzed the entry-wise error of spectral algorithms used for ranking items based on pairwise comparisons. The study focuses on the Bradley-Terry-Luce (BTL) model and investigates how performance is aff…
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Spectral algorithms in large dimensions reveal three learning curve regimes
A new research paper published on arXiv explores the learning curves and benign overfitting phenomena in spectral algorithms within large-dimensional settings. The study characterizes the excess risk across different re…