Feb. 20, 2024, 5:43 a.m. | Ruicheng Ao, Hongyu Chen, David Simchi-Levi, Feng Zhu

cs.LG updates on arXiv.org arxiv.org

arXiv:2402.11425v1 Announce Type: cross
Abstract: We consider the problem of online local false discovery rate (FDR) control where multiple tests are conducted sequentially, with the goal of maximizing the total expected number of discoveries. We formulate the problem as an online resource allocation problem with accept/reject decisions, which from a high level can be viewed as an online knapsack problem, with the additional uncertainty of random budget replenishment. We start with general arrival distributions and propose a simple policy that …

abstract arxiv control cs.lg decisions discoveries discovery false fdr math.oc math.pr multiple rate stat.me tests total type

Software Engineer for AI Training Data (School Specific)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Python)

@ G2i Inc | Remote

Software Engineer for AI Training Data (Tier 2)

@ G2i Inc | Remote

Data Engineer

@ Lemon.io | Remote: Europe, LATAM, Canada, UK, Asia, Oceania

Artificial Intelligence – Bioinformatic Expert

@ University of Texas Medical Branch | Galveston, TX

Lead Developer (AI)

@ Cere Network | San Francisco, US