[author]Hal Daume III, Piyush Rai, Avishek Saha and Suresh Venkatasubramanian[/author]
Fourteenth International Conference on Artificial Intelligence and Statistics (AISTATS), 2011.

Abstract:

In this paper, we propose an online multitask learning framework where the weight vectors are updated in an adaptive fashion based on inter-task relatedness. Our work is in contrast with earlier work on online multitask learning where the authors use a fixed interaction matrix of tasks to derive (fixed) update rules for all the tasks. In this work, we propose to update this interaction matrix itself in an adaptive fashion so that the weight vector updates are no longer fixed but are instead adaptive. Our framework can be extended to an active learning setting where the informativeness of an incoming instance across all the tasks can be evaluated using this adaptive interaction matrix. Empirical results on standardized datasets show improved performance in terms of accuracy, label complexity and number of mistakes made.

Notes:
Initially presented as Active Online Multitask Learning, in the Budgeted Learning Workshop (in conjunction with ICML 2010)