5 citations · 7 across the 4 of their papers we have counts for
4 papers
Accelerated Gradient Algorithms with Adaptive Subspace Search for Instance-Faster Optimization
Yuanshi Liu, Hanzhen Zhao, Yang Xu +2
Gradient-based minimax optimal algorithms have greatly promoted the development of continuous optimization and machine learning. One seminal work due to Yurii Nesterov [Nes83a] est…
CORE: Common Random Reconstruction for Distributed Optimization with Provable Low Communication Complexity
Pengyun Yue, Hanzhen Zhao, Cong Fang +4
With distributed machine learning being a prominent technique for large-scale machine learning tasks, communication complexity has become a major bottleneck for speeding up trainin…
Zeroth-order Optimization with Weak Dimension Dependency
Pengyun Yue, Long Yang, Cong Fang +1
Zeroth-order optimization is a fundamental research topic that has been a focus of various learning tasks, such as black-box adversarial attacks, bandits, and reinforcement learnin…
On the Lower Bound of Minimizing Polyak-Łojasiewicz Functions
Pengyun Yue, Cong Fang, Zhouchen Lin
Polyak-Łojasiewicz (PL) [Polyak, 1963] condition is a weaker condition than the strong convexity but suffices to ensure a global convergence for the Gradient Descent algorithm. In…