A0880
Title: Individual and interactive constrained online selection
Authors: Changliang Zou - Nankai University (China) [presenting]
Abstract: Real-time decision-making gets more attention in the big data era. The focus is on 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. A new decision rule enables finding 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, and diversity of selected samples. The key elements of the 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.
