Unverified50% confidenceFactExact time
In finite-action-set linear bandit problems, the adversarial environment requires the algorithm to guarantee ~O(√T) regret, and the stochastic environment expects O(log T)-level regret.
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11/3/2026
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UnverifiedIn standard regret minimization, the regret upper bound typically takes the form O(√T), whereas under the tilted sampling framework, sampling regret is dominated by the square of the confidence width, yielding logarithmic-level regret O(log T).76% similarUnverifiedAlgorithms such as online gradient descent can guarantee O(√T) regret under convex or approximately convex assumptions.75% similarUnverifiedThe classic Hedge algorithm achieves O(√(T log N)) regret under oblivious input, and this bound is tight in general.71% similarUnverifiedThe distribution shift problem in behavior cloning causes error accumulation, with the expected total loss after T steps being O(εT²) rather than the linear O(εT).69% similarUnverifiedThis universal lower bound delineates the boundary for kernel bandit algorithm design: the regret performance of any new algorithm cannot surpass Ω(√(Tγ_T/log T)).67% similar
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