In multi-task learning, using task grouping structure has been shown to be effective in preventing inappropriate knowledge transfer among unrelated tasks. However, the group structure often has to be predetermined using prior knowledge or heuristics, which has no theoretical guarantee and could lead to unsatisfactory learning performance. In this paper, we present a flexible multi-task learning framework to identify latent grouping structures under agnostic settings, where the prior of the latent subspace is unknown to the learner. In particular, we relax the latent subspace to be full rank, while imposing sparsity and orthogonality on the representation coefficients of target models. As a result, the target models still lie on a low dimensional subspace spanned by the selected basis tasks, and the structure of the latent task subspace is fully determined by the data. The final learning process is formulated as a joint optimization procedure over both the latent space and the target models. Besides providing proofs of theoretical guarantee on learning performance, we also conduct empirical evaluations on both synthetic and real data. Experimental results and comparisons with competing approaches corroborate the effectiveness of the proposed method.
['Shi Zhong', 'Jian Pu', 'Yu-Gang Jiang', 'Rui Feng', 'Xiangyang Xue']
Flexible multi-task learning with latent task grouping