Many sequential decision making problems can be formulated as an adaptive
submodular maximization problem. However, most of existing studies in this
field focus on pool-based setting, where one can pick items in any order, and
there have been few studies for the stream-based setting where items arrive in
an arbitrary order and one must immediately decide whether to select an item or
not upon its arrival. In this paper, we introduce a new class of utility
functions, semi-policywise submodular functions. We develop a series of
effective algorithms to maximize a semi-policywise submodular function under
the stream-based setting.

本文介绍了一种新的半策略次模函数类，针对流式问题，开发了一系列有效的算法来最大化其性能。

流式自适应次模最大化

Streaming Adaptive Submodular Maximization

We consider the problem of learning when obtaining the training labels is
costly, which is usually tackled in the literature using active-learning
techniques. These approaches provide strategies to choose the examples to label
before or during training. These strategies are usually based on heuristics or
even theoretical measures, but are not learned as they are directly used during
training. We design a model which aims at \textit{learning active-learning
strategies} using a meta-learning setting. More specifically, we consider a
pool-based setting, where the system observes all the examples of the dataset
of a problem and has to choose the subset of examples to label in a single
shot. Experiments show encouraging results.

本文研究了代价昂贵的标注问题，并提出了一种使用元学习技术来学习主动学习策略的模型，在池化的场景下使用。实验获得了鼓舞人心的结果。