3 citations · 4 across the 11 of their papers we have counts for
11 papers
DUET: Decentralized Bilevel Optimization without Lower-Level Strong Convexity
Zhen Qin, Zhuqing Liu, Songtao Lu +2
Decentralized bilevel optimization (DBO) provides a powerful framework for multi-agent systems to solve local bilevel tasks in a decentralized fashion without the need for a centra…
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…
STIMULUS: Achieving Fast Convergence and Low Sample Complexity in Stochastic Multi-Objective Learning
Zhuqing Liu, Chaosheng Dong, Michinari Momma +5
Recently, multi-objective optimization (MOO) has gained attention for its broad applications in ML, operations research, and engineering. However, MOO algorithm design remains in i…
Federated Multi-Objective Learning
Haibo Yang, Zhuqing Liu, Jia Liu +2
In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications. However, existing algorithms i…
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…
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization
Peiwen Qiu, Yining Li, Zhuqing Liu +5
Decentralized bilevel optimization has received increasing attention recently due to its foundational role in many emerging multi-agent learning paradigms (e.g., multi-agent meta-l…
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…
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data
Xin Zhang, Minghong Fang, Zhuqing Liu +3
Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. A…
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…
An empirical learning-based validation procedure for simulation workflow
Zhuqing Liu, Liyuanjun Lai, Lin Zhang
Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form…