217 citations · 612 across the 51 of their papers we have counts for
6 papers · 1 filter
Leveraging Two Reference Functions in Block Bregman Proximal Gradient Descent for Non-convex and Non-Lipschitz Problems
Tianxiang Gao, Songtao Lu, Jia Liu +1
In the applications of signal processing and data analytics, there is a wide class of non-convex problems whose objective function is freed from the common global Lipschitz continu…
Detecting Cyberattacks in Industrial Control Systems Using Online Learning Algorithms
Guangxia Lia, Yulong Shena, Peilin Zhaob +4
Industrial control systems are critical to the operation of industrial facilities, especially for critical infrastructures, such as refineries, power grids, and transportation syst…
Communication-Efficient Network-Distributed Optimization with Differential-Coded Compressors
Xin Zhang, Jia Liu, Zhengyuan Zhu +1
Network-distributed optimization has attracted significant attention in recent years due to its ever-increasing applications. However, the classic decentralized gradient descent (D…
Hierarchical Feature-Aware Tracking
Wenhua Zhang, Licheng Jiao, Jia Liu
In this paper, we propose a hierarchical feature-aware tracking framework for efficient visual tracking. Recent years, ensembled trackers which combine multiple component trackers…
Byzantine-Resilient Stochastic Gradient Descent for Distributed Learning: A Lipschitz-Inspired Coordinate-wise Median Approach
Haibo Yang, Xin Zhang, Minghong Fang +1
In this work, we consider the resilience of distributed algorithms based on stochastic gradient descent (SGD) in distributed learning with potentially Byzantine attackers, who coul…
Combinatorial Sleeping Bandits with Fairness Constraints
Fengjiao Li, Jia Liu, Bo Ji
The multi-armed bandit (MAB) model has been widely adopted for studying many practical optimization problems (network resource allocation, ad placement, crowdsourcing, etc.) with u…