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It is shown that test-time communication can substantially outperform independent parallel attempts on challenging tasks, where sharing a breakthrough can push the whole group forward, and gains compound with scale, suggesting gains compound with scale.
A smoothed-analysis framework that requires a learner to compete only with the best classifier that is robust to small random Gaussian perturbation is introduced, and the first algorithm for agnostic learning intersections of halfspaces in time is obtained, where $γ$ is the margin parameter.
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