The paper derives (with a flawed Lebesgue-integral argument) that entropy minimization performs two-level set-cover clustering and introduces URECA, a union-find clustering loss that improves few-shot code-search adaptation.
L ipschitz continuity in model-based reinforcement learning
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URECA: The Chain of Two Minimum Set Cover Problems exists behind Adaptation to Shifts in Semantic Code Search
The paper derives (with a flawed Lebesgue-integral argument) that entropy minimization performs two-level set-cover clustering and introduces URECA, a union-find clustering loss that improves few-shot code-search adaptation.