Presents CQB-η-2 algorithm achieving 𝒪̃(T^{-1/2}) queue length regret in contextual queueing bandits under stochastic contexts, with matching Ω(T^{-1/2}) lower bound.
Zixian Yang, R Srikant, and Lei Ying
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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Presents a model-based approach for estimating per-HTTP-request resource consumption and CO2e via offline benchmarking to derive endpoint-specific models evaluated online in an nginx extension.
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Algorithm for Contextual Queueing Bandits with Rate-Optimal Queue Length Regret
Presents CQB-η-2 algorithm achieving 𝒪̃(T^{-1/2}) queue length regret in contextual queueing bandits under stochastic contexts, with matching Ω(T^{-1/2}) lower bound.
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Measure Once, Model Everywhere: Model-Based Per-Request Resource Consumption for HTTP
Presents a model-based approach for estimating per-HTTP-request resource consumption and CO2e via offline benchmarking to derive endpoint-specific models evaluated online in an nginx extension.