FSCLB scales federated linear contextual bandits with sketching to achieve over 90% lower computation and communication costs while preserving a near-optimal regret bound of O(sqrt(l d T)).
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The paper organizes repository-level retrieval-augmented code generation into a unified framework covering retrieval substrate, control regime, and evaluation setting while summarizing strategies, datasets, and challenges.
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Scaling Federated Linear Contextual Bandits via Sketching
FSCLB scales federated linear contextual bandits with sketching to achieve over 90% lower computation and communication costs while preserving a near-optimal regret bound of O(sqrt(l d T)).
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Retrieval-Augmented Code Generation: A Survey with Focus on Repository-Level Approaches
The paper organizes repository-level retrieval-augmented code generation into a unified framework covering retrieval substrate, control regime, and evaluation setting while summarizing strategies, datasets, and challenges.