AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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COVariate-ovERlap regularized multi-task learning (COVER) is proposed to address covariate and posterior heterogeneity and achieves the lowest response-averaged prediction error among the compared methods and reveals tissue-pair integration patterns.
This work introduces an RKHS localization method that learns a data-adaptive weight from covariates and reformulates the target conditional moment at the target point as a weighted unconditional moment, yielding a prediction-powered estimator and confidence interval that reduce variance when the predictor is informativ...
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