In this study, we propose a method Distributionally Robust Safe Screening
(DRSS), for identifying unnecessary samples and features within a DR covariate
shift setting. This method effectively combines DR learning, a paradigm aimed
at enhancing model robustness against variations in data distribution, with
safe screening (SS), a sparse optimization technique designed to identify
irrelevant samples and features prior to model training. The core concept of
the DRSS method involves reformulating the DR covariate-shift problem as a
weighted empirical risk minimization problem, where the weights are subject to
uncertainty within a predetermined range. By extending the SS technique to
accommodate this weight uncertainty, the DRSS method is capable of reliably
identifying unnecessary samples and features under any future distribution
within a specified range. We provide a theoretical guarantee of the DRSS method
and validate its performance through numerical experiments on both synthetic
and real-world datasets.

我们提出了一种名为分布鲁棒安全筛选（DRSS）的方法，用于在 DR 协变量转变设置中识别不必要的样本和特征。该方法有效地将 DR 学习与安全筛选相结合，对模型训练之前的无关样本和特征进行了稀疏优化，实现了在特定范围内可靠地识别任何未来分布的不必要样本和特征。我们提供了 DRSS 方法的理论保证，并通过在合成和现实世界数据集上进行数值实验证实了其性能。

分布鲁棒安全筛选

Distributionally Robust Safe Screening

The paper presents the IWCS 2019 shared task on semantic parsing where the
goal is to produce Discourse Representation Structures (DRSs) for English
sentences. DRSs originate from Discourse Representation Theory and represent
scoped meaning representations that capture the semantics of negation, modals,
quantification, and presupposition triggers. Additionally, concepts and
event-participants in DRSs are described with WordNet synsets and the thematic
roles from VerbNet. To measure similarity between two DRSs, they are
represented in a clausal form, i.e. as a set of tuples. Participant systems
were expected to produce DRSs in this clausal form. Taking into account the
rich lexical information, explicit scope marking, a high number of shared
variables among clauses, and highly-constrained format of valid DRSs, all these
makes the DRS parsing a challenging NLP task. The results of the shared task
displayed improvements over the existing state-of-the-art parser.

该论文介绍了 2019 IWCS 分享任务的语义分析，目的是为英语句子生成话语表示结构（DRSs）。