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MASCOT outperforms traditional methods by preserving early-rank recall under complex diversity constraints, achieving R@10 of 94.10% while MS-DPP collapses under similar conditions.
Achieving sublinear cumulative Wasserstein regret in online distributional prediction without any parametric model for drift or corruption is a game-changer for adaptive learning systems.
