Proposes a constrained stochastic compositional gradient descent algorithm for queuing system design formulated as compositional stochastic programs, along with non-asymptotic iteration complexity bounds.
Controlling the t he bias-variance tradeoff via coherent risk for robust lear ning with kernels,
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Optimal Design of Queuing Systems via Compositional Stochastic Programming
Proposes a constrained stochastic compositional gradient descent algorithm for queuing system design formulated as compositional stochastic programs, along with non-asymptotic iteration complexity bounds.