【报告摘要】 Real-time decision-making gets more attention in the big data era. Here, we consider the problem of sample selection in the online setting, where one encounters a possibly infinite sequence of individuals collected over time with covariate information available. The goal is to select samples of interest that are characterized by their unobserved responses until the user-specified stopping time. We derive a new decision rule that enables us to find more preferable samples that meet practical requirements by simultaneously controlling two types of general constraints: individual and interactive constraints, which include the widely utilized False Selection Rate (FSR), cost limitations, diversity of selected samples, etc. The key elements of our approach involve quantifying the uncertainty of response predictions via predictive inference and addressing individual and interactive constraints in a sequential manner. Theoretical and numerical results demonstrate the effectiveness of the proposed method in controlling both individual and interactive constraints.

【报告人简介】任好洁是上海交通大学数学科学学院长聘教轨副教授，18年博士毕业于南开大学，随后在宾州州立大学从事博士后研究。她的研究方向包括预测推断、统计异常探查、在线学习与监控、高维数据推断等。在JASA，Biometrika等杂志和机器学习顶会ICML，NeurIPS上发表多篇学术论文。