Sublime generalizes Count-Min and Count Sketch with dynamically elongating counters and expanding counter arrays to deliver sublinear error growth and lower memory use on skewed unbounded streams.
Sur les fonctions convexes et les inégalités entre les valeurs moyennes,
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Derives generalization bounds for quantum learning via quantum and classical Rényi divergences, with a new modified sandwich quantum Rényi divergence shown to outperform the Petz version analytically and numerically.
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Sublime: Sublinear Error & Space for Unbounded Skewed Streams
Sublime generalizes Count-Min and Count Sketch with dynamically elongating counters and expanding counter arrays to deliver sublinear error growth and lower memory use on skewed unbounded streams.
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Generalization Bounds for Quantum Learning via R\'enyi Divergences
Derives generalization bounds for quantum learning via quantum and classical Rényi divergences, with a new modified sandwich quantum Rényi divergence shown to outperform the Petz version analytically and numerically.