论文标题
自适应下调元学习
Adaptive Submodular Meta-Learning
论文作者
论文摘要
元学习在机器学习和人工智能社区中引起了人们的关注。在本文中，我们介绍并研究了一个自适应的子模型元学习问题。我们问题的输入是一组项目，其中每个项目都有一个最初未知的随机状态。观察项目状态的唯一方法是选择该项目。我们的目的是自适应地选择一组项目，这些项目在一组任务上实现最佳性能，在该任务中，每个任务都表示为自适应的suppoular函数，该功能将项目及其状态映射到实际数字。为了降低计算成本，同时为每个将来的任务保持个性化解决方案，我们首先根据先前观察到的任务选择一个初始解决方案集，然后在新任务到达时自适应地将其余项目添加到初始解决方案集中。与针对每个新任务计算全新解决方案的解决方案相比，我们的基于元学习的方法在测试时间导致较低的计算开销，因为初始解决方案集在训练阶段预先计算。为了解决这个问题，我们提出了一项两阶段的贪婪政策，并表明它在单调案例中达到了$ 1/2 $ $的近似比。对于非主持剂案例，我们制定了两相随机贪婪的政策，该政策达到了$ 1/32美元的近似比。
Meta-Learning has gained increasing attention in the machine learning and artificial intelligence communities. In this paper, we introduce and study an adaptive submodular meta-learning problem. The input of our problem is a set of items, where each item has a random state which is initially unknown. The only way to observe an item's state is to select that item. Our objective is to adaptively select a group of items that achieve the best performance over a set of tasks, where each task is represented as an adaptive submodular function that maps sets of items and their states to a real number. To reduce the computational cost while maintaining a personalized solution for each future task, we first select an initial solution set based on previously observed tasks, then adaptively add the remaining items to the initial solution set when a new task arrives. As compared to the solution where a brand new solution is computed for each new task, our meta-learning based approach leads to lower computational overhead at test time since the initial solution set is pre-computed in the training stage. To solve this problem, we propose a two-phase greedy policy and show that it achieves a $1/2$ approximation ratio for the monotone case. For the non-monotone case, we develop a two-phase randomized greedy policy that achieves a $1/32$ approximation ratio.
