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Near-Optimal Design of Experiments via Regret Minimization
International Conference on Machine Learning
We consider computationally tractable methods for the experimental design problem, where k out of n design points of dimension p are selected so that certain optimality criteria are approximately satisfied. Our algorithm finds a (1 + ε)approximate optimal design when k is a linear function of p; in contrast, existing results require k to be super-linear in p. Our algorithm also handles all popular optimality criteria, while existing ones only handle one or two such criteria. Numerical resultsdblp:conf/icml/Allen-ZhuLSW17 fatcat:wbhz6ktx2ndsvk33hd5ncxz3qq