86 citations · 131 across the 7 of their papers we have counts for
7 papers
Robust Fully-Asynchronous Methods for Distributed Training over General Architecture
Zehan Zhu, Ye Tian, Yan Huang +2
Perfect synchronization in distributed machine learning problems is inefficient and even impossible due to the existence of latency, package losses and stragglers. We propose a Rob…
Acceleration in Distributed Optimization under Similarity
Ye Tian, Gesualdo Scutari, Tianyu Cao +1
We study distributed (strongly convex) optimization problems over a network of agents, with no centralized nodes. The loss functions of the agents are assumed to be \textit{similar…
Distributed Algorithms for Composite Optimization: Unified Framework and Convergence Analysis
Jinming Xu, Ye Tian, Ying Sun +1
We study distributed composite optimization over networks: agents minimize a sum of smooth (strongly) convex functions, the agents' sum-utility, plus a nonsmooth (extended-valued)…
A Unified Contraction Analysis of a Class of Distributed Algorithms for Composite Optimization
Jinming Xu, Ying Sun, Ye Tian +1
We study distributed composite optimization over networks: agents minimize the sum of a smooth (strongly) convex function, the agents' sum-utility, plus a non-smooth (extended-valu…
Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
Jinming Xu, Ye Tian, Ying Sun +1
This paper proposes a novel family of primal-dual-based distributed algorithms for smooth, convex, multi-agent optimization over networks that uses only gradient information and go…
Asynchronous Decentralized Successive Convex Approximation
Ye Tian, Ying Sun, Gesualdo Scutari
We study decentralized asynchronous multiagent optimization over networks, modeled as static (possibly directed) graphs. The optimization problem consists of minimizing a (possibly…
Achieving Linear Convergence in Distributed Asynchronous Multi-agent Optimization
Ye Tian, Ying Sun, Gesualdo Scutari
This papers studies multi-agent (convex and \emph{nonconvex}) optimization over static digraphs. We propose a general distributed \emph{asynchronous} algorithmic framework whereby…