3 citations · 3 across the 9 of their papers we have counts for
9 papers
Federated learning with heavy-tailed gradient noise and communication noise: a variance-reduction based algorithm
Shengchao Zhao, Yongchao Liu
Federated learning (FL) is an emerging distributed machine learning paradigm that enables local devices to jointly train a global model while keeping data decentralized and private…
Distributed TD Tracking with Linear Function Approximation over Directed Communication Networks
Haocheng Yang, Shengchao Zhao, Yongchao Liu
We study the policy evaluation problem in multi-agent reinforcement learning (MARL) over directed communication networks, where agents cooperate with each other to explore an unkno…
Investigating Group Relative Policy Optimization for Diffusion Transformer based Text-to-Audio Generation
Yi Gu, Yanqing Liu, Chen Yang +1
Text-to-audio (T2A) generation has advanced considerably in recent years, yet existing methods continue to face challenges in accurately rendering complex text prompts, particularl…
Efficient Gradient Tracking Algorithms for Distributed Optimization Problems with Inexact Communication
Shengchao Zhao, Yongchao Liu
Distributed optimization problems usually face inexact communication issues induced by channel noise, communication quantization or differential privacy protection. Most existing a…
A Variance-Reduced Aggregation Based Gradient Tracking method for Distributed Optimization over Directed Networks
Shengchao Zhao, Siyuan Song, Yongchao Liu
This paper studies the distributed optimization problem over directed networks with noisy information-sharing. To resolve the imperfect communication issue over directed networks,…
Numerical Methods for Distributed Stochastic Compositional Optimization Problems with Aggregative Structure
Shengchao Zhao, Yongchao Liu
The paper studies the distributed stochastic compositional optimization problems over networks, where all the agents' inner-level function is the sum of each agent's private expect…
Distributed Stochastic Compositional Optimization Problems over Directed Networks
Shengchao Zhao, Yongchao Liu
We study the distributed stochastic compositional optimization problems over directed communication networks in which agents privately own a stochastic compositional objective func…
Asymptotic Properties of - Method with Diminishing Stepsize
Shengchao Zhao, Yongchao Liu
The popular /push-pull method for distributed optimization problem may unify much of the existing decentralized first-order methods based on gradient tracking techniq…
Asymptotic properties of dual averaging algorithm for constrained distributed stochastic optimization
Shengchao Zhao, Xing-Min Chen, Yongchao Liu
Considering the constrained stochastic optimization problem over a time-varying random network, where the agents are to collectively minimize a sum of objective functions subject t…