• Publications
  • Influence
Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition
This paper identifies strict saddle property for non-convex problem that allows for efficient optimization of orthogonal tensor decomposition, and shows that stochastic gradient descent converges to a local minimum in a polynomial number of iterations. Expand
Is Q-learning Provably Efficient?
Model-free reinforcement learning (RL) algorithms, such as Q-learning, directly parameterize and update value functions or policies without explicitly modeling the environment. They are typicallyExpand
How to Escape Saddle Points Efficiently
This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension, which shows that perturbed gradient descent can escape saddle points almost for free. Expand
Provably Efficient Reinforcement Learning with Linear Function Approximation
This paper proves that an optimistic modification of Least-Squares Value Iteration (LSVI) achieves regret, where d is the ambient dimension of feature space, H is the length of each episode, and T is the total number of steps, and is independent of the number of states and actions. Expand
On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems
This is the first nonasymptotic analysis for two-time-scale GDA in this setting, shedding light on its superior practical performance in training generative adversarial networks (GANs) and other real applications. Expand
No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis
A new framework that captures the common landscape underlying the common non-convex low-rank matrix problems including matrix sensing, matrix completion and robust PCA shows that all local minima are also globally optimal; no high-order saddle points exists. Expand
Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent
To the best of the knowledge, this is the first Hessian-free algorithm to find a second-order stationary point faster than GD, and also the first single-loop algorithm with a faster rate than GD even in the setting of finding a first- order stationary point. Expand
Reward-Free Exploration for Reinforcement Learning
An efficient algorithm is given that conducts episodes of exploration and returns near-suboptimal policies for an arbitrary number of reward functions, and a nearly-matching $\Omega(S^2AH^2/\epsilon^2)$ lower bound is given, demonstrating the near-optimality of the algorithm in this setting. Expand
Stochastic Cubic Regularization for Fast Nonconvex Optimization
The proposed algorithm efficiently escapes saddle points and finds approximate local minima for general smooth, nonconvex functions in only $\mathcal{\tilde{O}}(\epsilon^{-3.5})$ stochastic gradient and stochastically Hessian-vector product evaluations. Expand
What is Local Optimality in Nonconvex-Nonconcave Minimax Optimization?
A proper mathematical definition of local optimality for this sequential setting---local minimax is proposed, as well as its properties and existence results are presented. Expand