论文标题
MT-GBM：具有共享决策树的多任务梯度提升机
MT-GBM: A Multi-Task Gradient Boosting Machine with Shared Decision Trees
论文作者
论文摘要
尽管深度学习在计算机视觉和自然语言处理中取得了成功，但梯度提升的决策树（GBDT）仍然是使用表格数据（例如电子商务和金融科技）应用程序的最强大的工具之一。但是，将GBDT应用于多任务学习仍然是一个挑战。与可以共同学习多个任务中共享的潜在表示的深层模型不同，GBDT几乎无法学习共享的树结构。在本文中，我们提出了多任务梯度提升机（MT-GBM），这是一种基于GBDT的多任务学习方法。 MT-GBM可以根据多任务损失找到共享树结构并拆分分支。首先，它将多个输出分配给每个叶节点。接下来，它计算与每个输出（任务）相对应的梯度。然后，我们还提出了一种算法，以结合所有任务的梯度并更新树。最后，我们将MT-GBM应用于LightGBM。实验表明，我们的MT-GBM显着提高了主要任务的性能，这意味着所提出的MT-GBM是有效的。
Despite the success of deep learning in computer vision and natural language processing, Gradient Boosted Decision Tree (GBDT) is yet one of the most powerful tools for applications with tabular data such as e-commerce and FinTech. However, applying GBDT to multi-task learning is still a challenge. Unlike deep models that can jointly learn a shared latent representation across multiple tasks, GBDT can hardly learn a shared tree structure. In this paper, we propose Multi-task Gradient Boosting Machine (MT-GBM), a GBDT-based method for multi-task learning. The MT-GBM can find the shared tree structures and split branches according to multi-task losses. First, it assigns multiple outputs to each leaf node. Next, it computes the gradient corresponding to each output (task). Then, we also propose an algorithm to combine the gradients of all tasks and update the tree. Finally, we apply MT-GBM to LightGBM. Experiments show that our MT-GBM improves the performance of the main task significantly, which means the proposed MT-GBM is efficient and effective.
