65 citations · 367 across the 30 of their papers we have counts for
5 papers · 1 filter
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…
Finding Local Minima via Stochastic Nested Variance Reduction
Dongruo Zhou, Pan Xu, Quanquan Gu
We propose two algorithms that can find local minima faster than the state-of-the-art algorithms in both finite-sum and general stochastic nonconvex optimization. At the core of th…
Stochastic Nested Variance Reduction for Nonconvex Optimization
Dongruo Zhou, Pan Xu, Quanquan Gu
We study finite-sum nonconvex optimization problems, where the objective function is an average of nonconvex functions. We propose a new stochastic gradient descent algorithm b…
Stochastic Variance-Reduced Cubic Regularized Newton Method
Dongruo Zhou, Pan Xu, Quanquan Gu
We propose a stochastic variance-reduced cubic regularized Newton method for non-convex optimization. At the core of our algorithm is a novel semi-stochastic gradient along with a…
Stochastic Variance-Reduced Hamilton Monte Carlo Methods
Difan Zou, Pan Xu, Quanquan Gu
We propose a fast stochastic Hamilton Monte Carlo (HMC) method, for sampling from a smooth and strongly log-concave distribution. At the core of our proposed method is a variance r…