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University of Toronto and Vector Institute, University of Hong Kong
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Black-box optimization can now safely leverage noisy, low-cost gradient approximations to bridge the gap between $O(d/T)$ zeroth-order and $O(1/T)$ first-order rates without stalling when guidance fails.
Capacity constraints can be effectively managed in online convex optimization, allowing for standard regret rates to be achieved even with limited tracking resources.
