243 citations · 392 across the 23 of their papers we have counts for
5 papers · 1 filter
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…
Task-Robust Pre-Training for Worst-Case Downstream Adaptation
Jianghui Wang, Yang Chen, Xingyu Xie +2
Pre-training has achieved remarkable success when transferred to downstream tasks. In machine learning, we care about not only the good performance of a model but also its behavior…
Policy Representation via Diffusion Probability Model for Reinforcement Learning
Long Yang, Zhixiong Huang, Fenghao Lei +6
Popular reinforcement learning (RL) algorithms tend to produce a unimodal policy distribution, which weakens the expressiveness of complicated policy and decays the ability of expl…
PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium
Shihong Ding, Hanze Dong, Cong Fang +2
We consider the non-convex non-concave objective function in two-player zero-sum continuous games. The existence of pure Nash equilibrium requires stringent conditions, posing a ma…