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SPIN Processed News Frame: The Hype
Reoptimization Algorithms for Contextual Bandits with Knapsack Constraints
A new theoretical algorithm for contextual bandits with knapsack constraints achieves a tighter regret bound of $O((\ln T)^3 / T)$, improving upon prior $O(1/\sqrt{T})$ bounds in related dynamic-pricing settings.
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arXiv Machine Learning
Aug 13, 2026
SPIN Processed News Frame: The Hype
Bootstrap-Conditioned Action Selection with Tabular Foundation Models
Researchers propose BC-ICL, a method using frozen pre-trained tabular foundation models with bootstrap resampling and in-context learning to improve early-round decision-making performance in contextual bandits under sparse, biased, or cold-start data conditions.
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arXiv Machine Learning
Aug 10, 2026