no code implementations • 26 Sep 2024 • Chao Min, Yijia Wang, Bo Zhang, Xin Ma, Junyi Cui
From the model-agnostic perspective, this paper proposes a parallel structure network to extract important information from both dynamic and static data.
no code implementations • 2 Aug 2024 • Yijia Wang, Qianqian Xu, Yangbangyan Jiang, Siran Dai, Qingming Huang
In recent years, multi-view outlier detection (MVOD) methods have advanced significantly, aiming to identify outliers within multi-view datasets.
no code implementations • 11 Feb 2024 • Chaosheng Dong, Yijia Wang
This paper studies generalized inverse reinforcement learning (GIRL) in Markov decision processes (MDPs), that is, the problem of learning the basic components of an MDP given observed behavior (policy) that might not be optimal.
no code implementations • 7 Oct 2023 • Zhixuan Chu, Huaiyu Guo, Xinyuan Zhou, Yijia Wang, Fei Yu, Hong Chen, Wanqing Xu, Xin Lu, Qing Cui, Longfei Li, Jun Zhou, Sheng Li
Large language models (LLMs) show promise for natural language tasks but struggle when applied directly to complex domains like finance.
1 code implementation • 18 Jul 2023 • Hanyan Cao, Feng Pan, Yijia Wang, Pan Zhang
Our framework is general and can be applied to any error model and quantum codes with different topologies such as surface codes and quantum LDPC codes.
no code implementations • 22 Feb 2023 • Chao Min, Yijia Wang, Huohai Yang, Wei Zhao
In this study, a novel machine learning model is constructed to fuse the static geological information, dynamic well production history, and spatial information of the adjacent water injection wells.
no code implementations • 3 Jan 2023 • Yijia Wang, Daniel R. Jiang
We consider infinite horizon Markov decision processes (MDPs) with fast-slow structure, meaning that certain parts of the state space move "fast" (and in a sense, are more influential) while other parts transition more "slowly."
no code implementations • 17 Apr 2022 • Zhijian Wang, Qinmei Yao, Yijia Wang
Eigen mode selection ought to be a practical issue in some real game systems, as it is a practical issue in the dynamics behaviour of a building, bridge, or molecular, because of the mathematical similarity in theory.
no code implementations • ICLR 2022 • Haobo Fu, Weiming Liu, Shuang Wu, Yijia Wang, Tao Yang, Kai Li, Junliang Xing, Bin Li, Bo Ma, Qiang Fu, Yang Wei
The deep policy gradient method has demonstrated promising results in many large-scale games, where the agent learns purely from its own experience.
no code implementations • 27 Apr 2021 • Chaosheng Dong, Xiaojie Jin, Weihao Gao, Yijia Wang, Hongyi Zhang, Xiang Wu, Jianchao Yang, Xiaobing Liu
Deep learning models in large-scale machine learning systems are often continuously trained with enormous data from production environments.
no code implementations • 12 Oct 2020 • Chaosheng Dong, Yijia Wang, Bo Zeng
We study the problem of learning the objective functions or constraints of a multiobjective decision making model, based on a set of sequentially arrived decisions.
1 code implementation • 21 Oct 2019 • Yijia Wang, Matthias Poloczek, Daniel R. Jiang
Reinforcement learning in sparse-reward navigation environments with expensive and limited interactions is challenging and poses a need for effective exploration.
no code implementations • 16 Nov 2017 • Yijia Wang, Yan Wan, Zhijian Wang
Knowing the reflection of game theory and ethics, we develop a mathematical representation to bridge the gap between the concepts in moral philosophy (e. g., Kantian and Utilitarian) and AI ethics industry technology standard (e. g., IEEE P7000 standard series for Ethical AI).