Reviews: Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer

In this work, authors proposed a framework for multi-task learning, where there is assumed to be latent task space, and the learner simultaneously learn the latent task representation as well as the coefficients for these latent variables, namely the Latent Task Assignment Matrix (LTAM). Authors further imposed block-diagonal structure on the assignment matrix, and developed spectral regularizers for it. Authors then proposed a relaxed objective that can be optimized via a scheme similar to block coordinate descent. Authors also provided generalization learning guarantees as well as the structure recovery performance. Simulation experiments showed that the proposed algorithm can recover the true structure and provide improvement in prediction accuracy.