Multi-Task Learning with Covariate-Overlap Regularization
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.
Yang Sui, Qi Xu, Yang Bai et al.
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