greedy
A Smoothed Analysis of the Greedy Algorithm for the Linear Contextual Bandit Problem
Sampath Kannan, Jamie H. Morgenstern, Aaron Roth, Bo Waggoner, Zhiwei Steven Wu
Wegiveasmoothed analysis, showing that evenwhen contexts may be chosen by an adversary, small perturbations of the adversary's choices suffice for the algorithm to achieve "no regret", perhaps (depending on the specifics of the setting) with a constant amount of initial training data.
Chain-of-Thought Reasoning without Prompting
These methods, while effective, often involve manually intensive prompt engineering.
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ScalabledesignofError-CorrectingOutputCodes usingDiscreteOptimizationwithGraphColoring
Error-correcting codes have found many applications in machine learning.
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Constrained Robust Submodular Partitioning
Note submodular 1 e 1, which assignment Round-Robinx.
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