Our paper is accepted in 23^st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Research Track). Halifax, Nova Scotia, Canada.

August 13-17, 2017. (Acceptance rate = 131/748 = 17.5%).

Title: Multi-task Function-on-function Regression with Co-grouping Structured Sparsity. 

Authors: Pei Yang, Qi Tan, Jingrui He.

Abstract: The growing importance of functional data has fueled the rapid development of functional data analysis, which treats the infinite-dimensional data as continuous functions rather than discrete, finite-dimensional vectors. On the other hand, heterogeneity is an intrinsic property of functional data due to the variety of sources to collect the data. In this paper, we propose a novel multi-task function-on-function regression approach to model both the functionality and heterogeneity of data. The basic idea is to simultaneously model the relatedness among tasks and correlations among basis functions by using the co-grouping structured sparsity to encourage  similar tasks to behave similarly in shrinking the basis functions. The resulting optimization problem is challenging due to the non-smoothness and non-separability of the co-grouping structured sparsity. We present an efficient algorithm to solve the problem, and prove its separability, convexity, and global convergence. The proposed algorithm is applicable to a wide spectrum of structured sparsity regularized techniques, such as structured $\ell_{2,p}$ norm and structured Schatten $p$-norm. The effectiveness of the proposed approach is verified on benchmark functional data sets.

All accepted papers in KDD 2017:

http://www.kdd.org/kdd2017/accepted-papers