Improved Active Multi-Task Representation Learning via Lasso
This addresses the practical need for sparse and efficient source task selection in multi-task learning scenarios where target task data is scarce.
The paper tackles the problem of selecting source tasks for multi-task representation learning by proposing a L1-regularized strategy that provably reduces sample complexity compared to prior L2-based methods, achieving up to 30% improvement in experiments on real-world computer vision datasets.
To leverage the copious amount of data from source tasks and overcome the scarcity of the target task samples, representation learning based on multi-task pretraining has become a standard approach in many applications. However, up until now, most existing works design a source task selection strategy from a purely empirical perspective. Recently, \citet{chen2022active} gave the first active multi-task representation learning (A-MTRL) algorithm which adaptively samples from source tasks and can provably reduce the total sample complexity using the L2-regularized-target-source-relevance parameter $ν^2$. But their work is theoretically suboptimal in terms of total source sample complexity and is less practical in some real-world scenarios where sparse training source task selection is desired. In this paper, we address both issues. Specifically, we show the strict dominance of the L1-regularized-relevance-based ($ν^1$-based) strategy by giving a lower bound for the $ν^2$-based strategy. When $ν^1$ is unknown, we propose a practical algorithm that uses the LASSO program to estimate $ν^1$. Our algorithm successfully recovers the optimal result in the known case. In addition to our sample complexity results, we also characterize the potential of our $ν^1$-based strategy in sample-cost-sensitive settings. Finally, we provide experiments on real-world computer vision datasets to illustrate the effectiveness of our proposed method.