Real-Time Selection Under General Constraints via Predictive Inference

Yuyang Huo, Lin Lu, Haojie Ren, Changliang Zou

Keywords: Online multiple testing; Predictive inference; False selection rate; Individual and interactive constraints; Local false discovery rate.

TL;DR: We address online sample selection, introducing II-COS, a decision rule that efficiently identifies preferable samples meeting practical requirements by managing individual and interactive constraints.

Abstract: 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, and diversity of selected samples. 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.

Primary Area: Other (please use sparingly, only use the keyword field for more details)

Submission Number: 9274