55 citations · 484 across the 40 of their papers we have counts for
7 papers · 1 filter
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…
Distributed Proximal Gradient Algorithm for Partially Asynchronous Computer Clusters
Yi Zhou, Yaoliang Yu, Wei Dai +2
With ever growing data volume and model size, an error-tolerant, communication efficient, yet versatile distributed algorithm has become vital for the success of many large-scale m…
Conditional Accelerated Lazy Stochastic Gradient Descent
Guanghui Lan, Sebastian Pokutta, Yi Zhou +1
In this work we introduce a conditional accelerated lazy stochastic gradient descent algorithm with optimal number of calls to a stochastic first-order oracle and convergence rate…