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Information-Theoretic Upper Bounds for Deterministic Noise in Zeroth-Order Convex Optimization
We study zeroth-order convex optimization on Euclidean balls with function values corrupted by a fixed, uniformly bounded deterministic perturbation. For a query budget $T$, the maximum admissible level of noise (MALN) is the supremum of noise levels for which an $\eps$-accurate solution can be foun
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