212 citations · 595 across the 39 of their papers we have counts for
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Lower Bounds for Smooth Nonconvex Finite-Sum Optimization
Dongruo Zhou, Quanquan Gu
Smooth finite-sum optimization has been widely studied in both convex and nonconvex settings. However, existing lower bounds for finite-sum optimization are mostly limited to the s…
Stochastic Recursive Variance-Reduced Cubic Regularization Methods
Dongruo Zhou, Quanquan Gu
Stochastic Variance-Reduced Cubic regularization (SVRC) algorithms have received increasing attention due to its improved gradient/Hessian complexities (i.e., number of queries to…
Sample Efficient Stochastic Variance-Reduced Cubic Regularization Method
Dongruo Zhou, Pan Xu, Quanquan Gu
We propose a sample efficient stochastic variance-reduced cubic regularization (Lite-SVRC) algorithm for finding the local minimum efficiently in nonconvex optimization. The propos…
Third-order Smoothness Helps: Even Faster Stochastic Optimization Algorithms for Finding Local Minima
Yaodong Yu, Pan Xu, Quanquan Gu
We propose stochastic optimization algorithms that can find local minima faster than existing algorithms for nonconvex optimization problems, by exploiting the third-order smoothne…