Unverified50% confidenceFactExact time
The regret rates of the exponentiated weights algorithm and the algorithm of Tseng et al. (2025) are already optimal in their polynomial dependence on the dimension d, matching the lower bound.
1
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50%
Confidence
Long-term
Relevance
10/16/2026
First Seen
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Related Claims
UnverifiedSince the joint lower bound shows that the dependence on (d, δ, ε) cannot be optimized in a decomposed manner, the trade-offs in algorithm design should be placed on improving constants and practical sample reuse efficiency, rather than attempting to break through the product structure of the three in worst-case guarantees.77% similarUnverifiedAlgorithm selection must match the nature of the problem: gradient descent is suited for smooth, differentiable, continuous optimization, while gradient-free methods are suited for discrete, step-wise, or highly noisy objective functions.72% 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.69% similarUnverifiedThe GPTQ quantization algorithm was proposed by Tim Dettmers et al. in 2023, optimizing quantization error layer by layer through second-order gradient information.69% similarUnverifiedIn the non-convex, high-dimensional, small-accuracy regime, the reasonable goal for zeroth-order algorithms is linear in d and close to ε^{-4} in accuracy, rather than pursuing sublinear dimension.69% similar
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