Non-adaptive preprocessing for discrete log and similar problems cannot beat O(sqrt(N)) online time without Omega(sqrt(N)) advice bits, proven via a Shearer-like inequality for permutations.
A note on concentration of submodular functions
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abstract
We survey a few concentration inequalities for submodular and fractionally subadditive functions of independent random variables, implied by the entropy method for self-bounding functions. The power of these concentration bounds is that they are dimension-free, in particular implying standard deviation O(\sqrt{\E[f]}) rather than O(\sqrt{n}) which can be obtained for any 1-Lipschitz function of n variables.
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Non-Adaptive Cryptanalytic Time-Space Lower Bounds via a Shearer-like Inequality for Permutations
Non-adaptive preprocessing for discrete log and similar problems cannot beat O(sqrt(N)) online time without Omega(sqrt(N)) advice bits, proven via a Shearer-like inequality for permutations.