Large Dimensional Analysis and Improvement of Multi Task Learning

Multi Task Learning (MTL) efficiently leverages useful information c ontained in multiple related tasks to help improve the generalization performance of all tasks. This article conducts a large dimensional analysis of a simple but, as we shall see, extremely powerful when carefully tuned, Least Square Support Vector Machine (LSS VM) version of MTL, in the regime where the dimension p of the data and their number n grow large at the same rate. Under mild assumptions on the input data, the theoretical analysis o f the MTL-LSSVM algorithm first reveals the "sufficient statistics" exploited by the alg orithm and their interaction at work. These results demonstrate, as a striking consequ ence, that the standard approach to MTL-LSSVM is largely suboptimal, can lead to severe effe cts of negative transfer but that these impairments are easily corrected. These correctio ns are turned into an improved MTL-LSSVM algorithm which can only benefit from additional data, and the theoretical performance of which is also analyzed. As evidenced and theoretically sustained in numerous recent works, these large dimensional results are robust to broad ranges of data distributions, w hich our present experiments corroborate. Specifically, the article reports a systematic ally close behavior between theoretical and empirical performances on popular datasets, wh ich is strongly suggestive of the applicability of the proposed carefully tuned MTL-LSSVM method to real data. This fine-tuning is fully based on the theoretical analysis and does not in p articular require any cross validation procedure. Besides, the reported performance s on real datasets almost systematically outperform much more elaborate and less intuitive state -of-the-art multi-task and transfer learning methods.