Researchers have analyzed the two-action apple-tasting problem, a scenario where a learner chooses between revealing information or taking a blind action, incurring costs for switching between them. They established that the oblivious minimax expected regret for this problem scales with the square root of time, specifically between $\frac{1}{2\sqrt3}\sqrt T$ and $2\sqrt3\sqrt T$. This finding resolves a long-standing question regarding the classification of feedback graphs in this context, demonstrating that a previously suspected $\Omega(T^{2/3})$ obstruction does not exist. AI
IMPACT Refines theoretical understanding of decision-making under uncertainty with switching costs, potentially informing agent design.
RANK_REASON Academic paper published on arXiv detailing a theoretical problem in machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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