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Indian Institute of Technology Kharagpur, Mohamed bin Zayed University of Artificial Intelligence
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Achieving a total-sample rate improvement from $T^{-1/4+o(1)}$ to $T^{-1/2+o(1)}$ could redefine efficiency benchmarks in stochastic approximation methods.
The price of strict fairness in bandit problems can lead to an unavoidable penalty that scales with the number of actions, revealing critical insights for designing fair algorithms.
