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Lecture Notes on Spectral Graph Methods

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arxiv 1608.04845 v1 pith:SDEVGQFC submitted 2016-08-17 cs.DS stat.ML

classification cs.DSstat.ML
keywords graphlecturemethodsnotesspectralberkeleyclassduring
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These are lecture notes that are based on the lectures from a class I taught on the topic of Spectral Graph Methods at UC Berkeley during the Spring 2015 semester.

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Cited by 2 Pith papers

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    A Fisher random walk weighted residual estimator achieves semiparametric efficient confidence intervals for contextual Bradley-Terry-Luce preference comparisons with flexible score estimators.

  2. Spectral Estimation with Free Decompression

    stat.ML 2025-06 conditional novelty 6.0 of 10

    Free decompression evolves a small submatrix spectrum into an estimate of a large matrix spectrum using a PDE derived from free probability, the Nica-Speicher free compression theorem.

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