2022/07/13 by Cheng Chen, Yi Li, Chen, Cheng +3
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Algorithms #Optimization and Search Problems
paper · pdf · doi:10.48550/arxiv.2207.05945
openalex publication_date 2022/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Active regression considers a linear regression problem where the learner receives a large number of data points but can only observe a small number of labels. Since online algorithms can deal with incremental training data and take advantage of low computational cost, we consider an online extension of the active regression problem: the learner receives data points one by one and immediately decides whether it should collect the corresponding labels. The goal is to efficiently maintain the regression of received data points with a small budget of label queries. We propose novel algorithms for this problem under ℓp loss where p∈[1,2]. To achieve a (1+ε)-approximate solution, our proposed algorithms only require O(ε-1 d log(nκ)) queries of labels, where n is the number of data points and κ is a quantity, called the condition number, of the data points. The numerical results verify our theoretical results and show that our methods have comparable performance with offline active regression algorithms.