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The Spiteful Greedy Swap Poisson Process retains its approximation guarantees even in adversarial environments, paving the way for robust online learning in submodular settings.
Achieving universal dynamic regret in online convex optimization without any prior knowledge of problem parameters could redefine the landscape of adaptive algorithms under heavy-tailed noise.
