4 citations · 4 across the 5 of their papers we have counts for
5 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…
Finite-Time Global Optimality Convergence in Deep Neural Actor-Critic Methods for Decentralized Multi-Agent Reinforcement Learning
Zhiyao Zhang, Myeung Suk Oh, FNU Hairi +3
Actor-critic methods for decentralized multi-agent reinforcement learning (MARL) facilitate collaborative optimal decision making without centralized coordination, thus enabling a…
Byzantine-Robust Decentralized Federated Learning
Minghong Fang, Zifan Zhang, Hairi +5
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without revealing their private training data. In conventional FL, the system foll…
Finite-Time Convergence and Sample Complexity of Actor-Critic Multi-Objective Reinforcement Learning
Tianchen Zhou, FNU Hairi, Haibo Yang +5
Reinforcement learning with multiple, potentially conflicting objectives is pervasive in real-world applications, while this problem remains theoretically under-explored. This pape…
Sample and Communication Efficient Fully Decentralized MARL Policy Evaluation via a New Approach: Local TD update
Fnu Hairi, Zifan Zhang, Jia Liu
In actor-critic framework for fully decentralized multi-agent reinforcement learning (MARL), one of the key components is the MARL policy evaluation (PE) problem, where a set of $N…