36 citations · 100 across the 5 of their papers we have counts for
5 papers
Random gradient extrapolation for distributed and stochastic optimization
Guanghui Lan, Yi Zhou
In this paper, we consider a class of finite-sum convex optimization problems defined over a distributed multiagent network with agents connected to a central server. In partic…
Critical Points of Neural Networks: Analytical Forms and Landscape Properties
Yi Zhou, Yingbin Liang
Due to the success of deep learning to solving a variety of challenging machine learning tasks, there is a rising interest in understanding loss functions for training neural netwo…
Characterization of Gradient Dominance and Regularity Conditions for Neural Networks
Yi Zhou, Yingbin Liang
The past decade has witnessed a successful application of deep learning to solving many challenging problems in machine learning and artificial intelligence. However, the loss func…
Convergence Analysis of Proximal Gradient with Momentum for Nonconvex Optimization
Qunwei Li, Yi Zhou, Yingbin Liang +1
In many modern machine learning applications, structures of underlying mathematical models often yield nonconvex optimization problems. Due to the intractability of nonconvexity, t…
Communication-Efficient Algorithms for Decentralized and Stochastic Optimization
Guanghui Lan, Soomin Lee, Yi Zhou
We present a new class of decentralized first-order methods for nonsmooth and stochastic optimization problems defined over multiagent networks. Considering that communication is a…