论文标题
在多任务学习中学习可解释的图形结构
Learning an Interpretable Graph Structure in Multi-Task Learning
论文作者
论文摘要
我们提出了一种新颖的方法，可以通过可解释和稀疏的图表共同执行任务之间的多任务学习并推断任务之间的内在关系。与现有的多任务学习方法不同，在预处理步骤中，不认为该图结构是先验或单独估计的。取而代之的是，我们的图与每个任务的模型参数同时学习，因此它反映了特定预测问题中任务之间的关键关系。我们表征了图形结构及其加权邻接矩阵，并表明可以优化整体目标直至收敛。我们还表明，通过嵌入多头径向基函数网络（RBFN）中，可以将我们的方法简单地扩展到非线性形式。针对六种最先进的方法论，对合成数据和现实世界应用程序的广泛实验表明，我们的方法能够减少泛化错误，同时，在任务上揭示了稀疏的图表，该任务易于解释。
We present a novel methodology to jointly perform multi-task learning and infer intrinsic relationship among tasks by an interpretable and sparse graph. Unlike existing multi-task learning methodologies, the graph structure is not assumed to be known a priori or estimated separately in a preprocessing step. Instead, our graph is learned simultaneously with model parameters of each task, thus it reflects the critical relationship among tasks in the specific prediction problem. We characterize graph structure with its weighted adjacency matrix and show that the overall objective can be optimized alternatively until convergence. We also show that our methodology can be simply extended to a nonlinear form by being embedded into a multi-head radial basis function network (RBFN). Extensive experiments, against six state-of-the-art methodologies, on both synthetic data and real-world applications suggest that our methodology is able to reduce generalization error, and, at the same time, reveal a sparse graph over tasks that is much easier to interpret.
