Unverified50% confidenceFactExact time
Before this study, the best known polynomial-time algorithm for the online convex optimization problem of tracking a sequence of experts that switches k times had regret Õ(dk).
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50%
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Medium-term (~90 days)
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10/15/2026
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Valid until: 1/13/2027
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UnverifiedIn 2020, Bousquet and Warmuth gave an efficient algorithm for a discrete set of experts, whose regret reaches the information-theoretic lower bound, that is, when the comparator switches k times among m experts, the regret is Õ(md+k), and the computation is polynomial-time.72% similarUnverified精确的 K 近邻搜索（KNN）时间复杂度为 O(n×d)，在数百万级别记忆条目下延迟不可接受，ANN 算法通过牺牲极小精度换取数量级速度提升70% similarUnverifiedFor practical algorithm design, when the dimension d is large and the number of switches k is non-negligible, accepting Õ(dk)-level regret or seeking additional structural assumptions may be a more pragmatic direction than relying on discretization or exact optimization.70% similarUnverifiedThe d·log p sample complexity is the advantageous order possessed by previous sharp methods that relied on subset search, and HOST achieves this level without sacrificing polynomial runtime.69% similarUnverifiedBoth EKF and factor graphs assume that the timestamps of input data are accurate, and time offsets will be treated as spatial errors by these algorithms.68% similar
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