9 citations · 26 across the 12 of their papers we have counts for
12 papers
A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization
Zhiyao Zhang, Myeung Suk Oh, Zhen Qin +3
In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing works on BLO remain confined…
Multi-Objective Bilevel Learning
Zhiyao Zhang, Zhuqing Liu, Xin Zhang +3
As machine learning (ML) applications grow increasingly complex in recent years, modern ML frameworks often need to address multiple potentially conflicting objectives with coupled…
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Zhuqing Liu, Xin Zhang, Songtao Lu +1
Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning (ML). However, most existing min-max solution…
SAGDA: Achieving Communication Complexity in Federated Min-Max Learning
Haibo Yang, Zhuqing Liu, Xin Zhang +1
To lower the communication complexity of federated min-max learning, a natural approach is to utilize the idea of infrequent communications (through multiple local updates) same as…
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters
Zhuqing Liu, Xin Zhang, Jia Liu
To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed sto…
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks
Zhuqing Liu, Xin Zhang, Prashant Khanduri +2
In recent years, decentralized bilevel optimization problems have received increasing attention in the networking and machine learning communities thanks to their versatility in mo…
Anarchic Federated Learning
Haibo Yang, Xin Zhang, Prashant Khanduri +1
Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabiliti…
GT-STORM: Taming Sample, Communication, and Memory Complexities in Decentralized Non-Convex Learning
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in system robustness, data privacy, and implementat…
Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach
Xin Zhang, Minghong Fang, Jia Liu +1
With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In thi…
Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (D…
Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach
Haibo Yang, Xin Zhang, Minghong Fang +1
In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who coul…
Distributed Linear Model Clustering over Networks: A Tree-Based Fused-Lasso ADMM Approach
Xin Zhang, Jia Liu, Zhengyuan Zhu
In this work, we consider to improve the model estimation efficiency by aggregating the neighbors' information as well as identify the subgroup membership for each node in the netw…