Cooperative learning combines the usual squared error loss of predictions with an agreement penalty to encourage the predictions from different data views to agree. By varying the weight of the agreement penalty, we get a continuum of solutions that include the wellknown early and late fusion approaches. Cooperative learning chooses the degree of agreement (or fusion) in an adaptive manner, using a validation set or crossvalidation to estimate test set prediction error. In the setting of cooperative regularized linear regression, the method combines the lasso penalty with the agreement penalty (Ding, D., Li, S., Narasimhan, B., Tibshirani, R. (2021) <doi:10.1073/pnas.2202113119>).
Package details 


Author  Daisy Yi Ding [aut], Robert J. Tibshirani [aut], Balasubramanian Narasimhan [aut, cre], Trevor Hastie [aut], Kenneth Tay [aut], James Yang [aut] 
Maintainer  Balasubramanian Narasimhan <naras@stanford.edu> 
License  GPL2 
Version  0.8 
Package repository  View on CRAN 
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