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Near-Optimal Design of Experiments via Regret Minimization
Zeyuan Allen-Zhu · Yuanzhi Li · Aarti Singh · Yining Wang
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+eps)-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 results on synthetic and real-world design problems verify the practical effectiveness of the proposed algorithm.
Summary/NotesAuthor Information
Zeyuan Allen-Zhu (Microsoft Research / Princeton / IAS)
Yuanzhi Li (Princeton University)
Aarti Singh (Carnegie Mellon University)
Yining Wang (CMU)
Related Events (a corresponding poster, oral, or spotlight)
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2017 Poster: Near-Optimal Design of Experiments via Regret Minimization »
Wed. Aug 9th 08:30 AM -- 12:00 PM Room Gallery #124
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