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Achieving near-optimal regret in adversarial $m$-set bandits without exponential space requirements could redefine efficiency benchmarks in combinatorial learning.
The last iterate of the stochastic subgradient method can achieve a significantly tighter optimization error bound, but only under strict noise assumptions.
