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Improved Dimension Dependence for Bandit Convex Optimization with Gradient Variations
Gradient-variation online learning has drawn increasing attention due to its deep connections to game theory and optimization. It has been studied extensively in the full-information setting, but is underexplored with bandit feedback. In this work, we focus on gradient variation in Bandit Convex Opt
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