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We prove lower bounds for higher-order methods in smooth non-convex finite-sum optimization.
A stochastic trust region method for non-convex minimization
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Lower bounds for non-convex stochastic optimization
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Accelerating stochastic gradient descent using predictive variance reduction
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Spider: Near-optimal non-convex optimization via stochastic path-integrated differential estimator
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Spiderboost and momentum: Faster variance reduction algorithms
Wang, Z., Ji, K., Zhou, Y., Liang, Y., and Tarokh, V. (2019) · 2019
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Lower bounds for smooth nonconvex finite-sum optimization
Zhou, D. and Gu, Q. (2019) · 2019
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Stochastic variance-reduced cubic regularization methods
Zhou, D., Xu, P., and Gu, Q. (2019) · 2019
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Second-order information in non-convex stochastic optimization: Power and limitations
Arjevani, Y., Carmon, Y., Duchi, J. C., Foster, D. J., Sekhari, A., and Sridharan, K. (2020) · 2020
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Non-convex finite-sum optimization via scsg methods
Lei, L., Ju, C., Chen, J., and Jordan, M. I. (2017) · 2017
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Lower bounds for finding stationary points i
Carmon, Y., Duchi, J. C., Hinder, O., and Sidford, A. (2019a)
Cited in the paper.
Lower bounds for finding stationary points ii: first-order methods
Carmon, Y., Duchi, J. C., Hinder, O., and Sidford, A. (2019b)
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Stochastic recursive variance-reduced cubic regularization methods
Zhou, D. and Gu, Q. (2020) · 2020
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