11 citations · 15 across the 5 of their papers we have counts for
5 papers
Unsupervised Domain Adaptation for Binary Classification with an Unobservable Source Subpopulation
Chao Ying, Jun Jin, Haotian Zhang +4
We study an unsupervised domain adaptation problem where the source domain consists of subpopulations defined by the binary label and a binary background (or environment) .…
Amortized Variational Deep Q Network
Haotian Zhang, Yuhao Wang, Jianyong Sun +1
Efficient exploration is one of the most important issues in deep reinforcement learning. To address this issue, recent methods consider the value function parameters as random var…
Learning to be Global Optimizer
Haotian Zhang, Jianyong Sun, Zongben Xu
The advancement of artificial intelligence has cast a new light on the development of optimization algorithm. This paper proposes to learn a two-phase (including a minimization pha…
On Hyper-parameter Tuning for Stochastic Optimization Algorithms
Haotian Zhang, Jianyong Sun, Zongben Xu
This paper proposes the first-ever algorithmic framework for tuning hyper-parameters of stochastic optimization algorithm based on reinforcement learning. Hyper-parameters impose s…
Adaptive Structural Hyper-Parameter Configuration by Q-Learning
Haotian Zhang, Jianyong Sun, Zongben Xu
Tuning hyper-parameters for evolutionary algorithms is an important issue in computational intelligence. Performance of an evolutionary algorithm depends not only on its operation…