no code implementations • 22 Mar 2024 • Zeliang Zhang, Mingqian Feng, Jinyang Jiang, Rongyi Zhu, Yijie Peng, Chenliang Xu
Gradient-based saliency maps are widely used to explain deep neural network decisions.
no code implementations • 18 Mar 2024 • Zeliang Zhang, Jinyang Jiang, Zhuo Liu, Susan Liang, Yijie Peng, Chenliang Xu
In this paper, we introduce an approximation technique for the likelihood ratio (LR) method to alleviate computational and memory demands in gradient estimation.
no code implementations • 1 Mar 2024 • Jinyang Jiang, Xiaotian Liu, Tao Ren, Qinghao Wang, Yi Zheng, Yufu Du, Yijie Peng, Cheng Zhang
We introduce a deep reinforcement learning (DRL) approach for solving management problems including inventory management, dynamic pricing, and recommendation.
no code implementations • 11 Feb 2024 • Tao Ren, Ruihan Zhou, Jinyang Jiang, Jiafeng Liang, Qinghao Wang, Yijie Peng
Existing alpha mining only employs the neural network agent, unable to utilize the structural information of the solution space.
no code implementations • 3 Feb 2024 • Zishi Zhang, Yijie Peng
We propose novel "clustering and conquer" procedures for the parallel large-scale ranking and selection (R&S) problem, which leverage correlation information for clustering to break the bottleneck of sample efficiency.
no code implementations • 1 Feb 2024 • Ruihan Zhou, L. Jeff Hong, Yijie Peng
We introduce AlphaRank, an artificial intelligence approach to address the fixed-budget ranking and selection (R&S) problems.
no code implementations • 15 May 2023 • Jinyang Jiang, Zeliang Zhang, Chenliang Xu, Zhaofei Yu, Yijie Peng
While backpropagation (BP) is the mainstream approach for gradient computation in neural network training, its heavy reliance on the chain rule of differentiation constrains the designing flexibility of network architecture and training pipelines.
1 code implementation • 12 May 2023 • Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative reward.
no code implementations • 6 May 2023 • Gongbo Zhang, Sihua Chen, Kuihua Huang, Yijie Peng
We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts.
no code implementations • 17 Feb 2023 • Zeliang Zhang, Jinyang Jiang, Minjie Chen, Zhiyuan Wang, Yijie Peng, Zhaofei Yu
Noise injection-based method has been shown to be able to improve the robustness of artificial neural networks in previous work.
no code implementations • 26 Apr 2022 • Gongbo Zhang, Yijie Peng, Yilong Xu
We consider the popular tree-based search strategy within the framework of reinforcement learning, the Monte Carlo Tree Search (MCTS), in the context of finite-horizon Markov decision process.
no code implementations • 27 Jan 2022 • Jinyang Jiang, Jiaqiao Hu, Yijie Peng
Classical reinforcement learning (RL) aims to optimize the expected cumulative rewards.
no code implementations • 20 Mar 2021 • Li Xiao, Yinhao Li, Luxi Qv, Xinxia Tian, Yijie Peng, S. Kevin Zhou
Segmentation of pathological images is essential for accurate disease diagnosis.
1 code implementation • 6 Feb 2021 • Li Xiao, Zeliang Zhang, Yijie Peng
Adding noises to artificial neural network(ANN) has been shown to be able to improve robustness in previous work.
no code implementations • 10 Dec 2020 • Haidong Li, Henry Lam, Zhe Liang, Yijie Peng
We consider a context-dependent ranking and selection problem.
Methodology
1 code implementation • 10 Dec 2020 • Haidong Li, Henry Lam, Yijie Peng
We consider a simulation optimization problem for a context-dependent decision-making.
Decision Making Methodology
1 code implementation • 31 Jan 2019 • Li Xiao, Yijie Peng, Jeff Hong, Zewu Ke, Shuhuai Yang
In this work, we propose a generalized likelihood ratio method capable of training the artificial neural networks with some biological brain-like mechanisms,. e. g., (a) learning by the loss value, (b) learning via neurons with discontinuous activation and loss functions.
no code implementations • 7 Oct 2017 • Yijie Peng, Edwin K. P. Chong, Chun-Hung Chen, Michael C. Fu
Under a Bayesian framework, we formulate the fully sequential sampling and selection decision in statistical ranking and selection as a stochastic control problem, and derive the associated Bellman equation.