5 citations · 19 across the 7 of their papers we have counts for
5 papers · 1 filter
Active Reinforcement Learning over MDPs
Qi Yang, Peng Yang, Ke Tang
The past decade has seen the rapid development of Reinforcement Learning, which acquires impressive performance with numerous training resources. However, one of the greatest chall…
On Performance Estimation in Automatic Algorithm Configuration
Shengcai Liu, Ke Tang, Yunwen Lei +1
Over the last decade, research on automated parameter tuning, often referred to as automatic algorithm configuration (AAC), has made significant progress. Although the usefulness o…
Stochastic Gradient Descent for Nonconvex Learning without Bounded Gradient Assumptions
Yunwen Lei, Ting Hu, Guiying Li +1
Stochastic gradient descent (SGD) is a popular and efficient method with wide applications in training deep neural nets and other nonconvex models. While the behavior of SGD is wel…
Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization
Yibo Zhang, Chao Qian, Ke Tang
In this paper, we study the problem of monotone (weakly) DR-submodular continuous maximization. While previous methods require the gradient information of the objective function, w…
Concept Drift Adaptation by Exploiting Historical Knowledge
Yu Sun, Ke Tang, Zexuan Zhu +1
Incremental learning with concept drift has often been tackled by ensemble methods, where models built in the past can be re-trained to attain new models for the current data. Two…