Near-optimal discrete optimization for experimental design: a regret minimization approach

Author:

Allen-Zhu Zeyuan,Li Yuanzhi,Singh Aarti,Wang Yining

Funder

National Science Foundation

Air Force Research Laboratory

Publisher

Springer Science and Business Media LLC

Subject

General Mathematics,Software

Reference50 articles.

1. Ageev, A.A., Sviridenko, M.I.: Pipage rounding: a new method of constructing algorithms with proven performance guarantee. J. Comb. Optim. 8(3), 307–328 (2004)

2. Allen-Zhu, Z., Li, Y.: Follow the compressed leader: faster online learning of eigenvectors and faster MMWU. In: Proceedings of the International Conference on Machine Learning (ICML). Full version available at arxiv:1701.01722 (2017)

3. Allen-Zhu, Z., Li, Y., Singh, A., Wang, Y.: Near-optimal design of experiments via regret minimization. In: Proceedings of the International Conference on Machine Learning (ICML), (2017)

4. Allen-Zhu, Z., Liao, Z., Orecchia, L.: Spectral sparsification and regret minimization beyond matrix multiplicative updates. In: Proceedings of Annual Symposium on the Theory of Computing (STOC), (2015)

5. Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1988)

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