2020/06/09 by Arun Kumar Kuchibhotla, Qinqing Zheng, Kuchibhotla, Arun Kumar +1
Computer Science · Decision Sciences · Mathematics · #Applications (stat.AP) #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Mathematics #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Probability and Risk Models #Risk and Portfolio Optimization #Statistical Methods and Inference #Statistics Theory (math.ST) #cs.AI #cs.LG #math.ST #stat.AP #stat.ML #stat.TH
paper · pdf · doi:10.48550/arxiv.2006.05022
Accepted to ICML 2021
openalex publication_date 2020/06/09 · arxiv created 2021/06/03 · arxiv updated 2021/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Many inference problems, such as sequential decision problems like A/B testing, adaptive sampling schemes like bandit selection, are often online in nature. The fundamental problem for online inference is to provide a sequence of confidence intervals that are valid uniformly over the growing-into-infinity sample sizes. To address this question, we provide a near-optimal confidence sequence for bounded random variables by utilizing Bentkus' concentration results. We show that it improves on the existing approaches that use the Cramér-Chernoff technique such as the Hoeffding, Bernstein, and Bennett inequalities. The resulting confidence sequence is confirmed to be favorable in both synthetic coverage problems and an application to adaptive stopping algorithms.