To achieve this, different from conventional methods which view the multiple responses as independent ones, we propose to consider the correlation of multiple responses with a novel twostep generation architecture, where the response bag {y} and each response y ∈ {y} are modeled by two separate features which are obtained in each generation phase respectively.
we assume a variable c ∈ R n representing the common feature of different responses and an unobserved latent variable z ∈ Z corresponding to the distinct feature for each y in the bag.