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Optimal Exploration is no harder than Thompson Sampling
In this work, we pose a natural question: is there an algorithm that can explore optimally and only needs the same computational primitives as Thompson Sampling?
Instance-optimal PAC Algorithms for Contextual Bandits
In the stochastic contextual bandit setting, regret-minimizing algorithms have been extensively researched, but their instance-minimizing best-arm identification counterparts remain seldom studied.
Exploration of Numerical Precision in Deep Neural Networks
Reduced numerical precision is a common technique to reduce computational cost in many Deep Neural Networks (DNNs).
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