2023/05/03 by Lau, Lap Chi, Wang, Robert, Zhou, Hong
#Computation (stat.CO) #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML)
paper · doi:10.48550/arxiv.2305.01942
We consider a general p-norm objective for experimental design problems that captures some well-studied objectives (D/A/E-design) as special cases. We prove that a randomized local search approach provides a unified algorithm to solve this problem for all p. This provides the first approximation algorithm for the general p-norm objective, and a nice interpolation of the best known bounds of the special cases.