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The cost of tracking changing comparators in exp-concave online learning is reduced by a factor of $d^{1/3}$, resolving the dimension's optimal dependence.
SVD meets random projections: PRAC offers a surprisingly effective recipe for compressing LLM activations, slashing memory by 36% while barely impacting performance.