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Variance Reduction
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities
Recently, min-max optimization problems have received increasing attention due to their wide range of applications in machine learning …
Zhuqing Liu
,
Xin Zhang
,
Songtao Lu
,
Jia Liu
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Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning
In recent years, decentralized bilevel optimization has gained significant attention thanks to its versatility in modeling a wide range …
Zhuqing Liu
,
Xin Zhang
,
Prashant Khanduri
,
Songtao Lu
,
Jia Liu
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SYNTHESIS: A semi-asynchronous path-integrated stochastic gradient method for distributed learning in computing clusters
To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both …
Zhuqing Liu
,
Xin Zhang
,
Jia Liu
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Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning
Cooperative multi-agent reinforcement learning (MARL) has found many scientific and engineering applications. In this paper, we focus on decentralized MARL policy evaluation with nonlinear function approximation and propose efficient optimization algorithms.
Xin Zhang
,
Zhuqing Liu
,
Jia Liu
,
Zhengyuan Zhu
,
Songtao Lu
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GT-STORM: Taming sample, communication, and memory complexities in decentralized non-convex learning
Decentralized nonconvex optimization has received increasing attention in recent years in machine learning due to its advantages in …
Xin Zhang
,
Jia Liu
,
Zhengyuan Zhu
,
Elizabeth Serena Bentley
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Low Sample and Communication Complexities in Decentralized Learning: A Triple Hybrid Approach
Decentralized optimization has received increasing attention in recent years, due to its advantages in system robustness, data privacy …
Xin Zhang
,
Jia Liu
,
Zhengyuan Zhu
,
Elizabeth Serena Bentley
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