HAL (France)
open access
Quantized Online Gradient Descent for constant-time caching with dynamic regret guarantees
Online learning provides a strong theoretical foundation for caching, but existing algorithms often rely on idealized arbitrary-precision computations and incur per-request overheads that scale with the catalog size, making them difficult to deploy in practice. To address this limitation, we introdu
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