23 citations · 119 across the 32 of their papers we have counts for
5 papers · 1 filter
Near-Optimal Decentralized Momentum Method for Nonconvex-PL Minimax Problems
Feihu Huang, Songcan Chen
Minimax optimization plays an important role in many machine learning tasks such as generative adversarial networks (GANs) and adversarial training. Although recently a wide variet…
Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
Feihu Huang, Chunyu Xuan, Xinrui Wang +2
Minimax optimization recently is widely applied in many machine learning tasks such as generative adversarial networks, robust learning and reinforcement learning. In the paper, we…
On Momentum-Based Gradient Methods for Bilevel Optimization with Nonconvex Lower-Level
Feihu Huang
Bilevel optimization is a popular two-level hierarchical optimization, which has been widely applied to many machine learning tasks such as hyperparameter learning, meta learning a…
FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging
Junyi Li, Feihu Huang, Heng Huang
Federated learning (FL) is an emerging learning paradigm to tackle massively distributed data. In Federated Learning, a set of clients jointly perform a machine learning task under…
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
Junyi Li, Feihu Huang, Heng Huang
Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms. However, its application in the Federated Learning setting remains relatively u…