no code implementations • 11 Feb 2024 • Xidong Feng, Ziyu Wan, Mengyue Yang, Ziyan Wang, Girish A. Koushik, Yali Du, Ying Wen, Jun Wang
Reinforcement Learning (RL) has shown remarkable abilities in learning policies for decision-making tasks.
no code implementations • 23 Jan 2024 • Jiarui Jin, Zexue He, Mengyue Yang, Weinan Zhang, Yong Yu, Jun Wang, Julian McAuley
Subsequently, we minimize the mutual information between the observation estimation and the relevance estimation conditioned on the input features.
1 code implementation • 21 Oct 2023 • Mengyue Yang, Xinyu Cai, Furui Liu, Weinan Zhang, Jun Wang
Under the hypothesis that the intrinsic latent factors follow some casual generative models, we argue that by learning a causal representation, which is the minimal sufficient causes of the whole system, we can improve the robustness and generalization performance of machine learning models.
1 code implementation • NeurIPS 2023 • Mengyue Yang, Zhen Fang, Yonggang Zhang, Yali Du, Furui Liu, Jean-Francois Ton, Jianhong Wang, Jun Wang
To capture the information of sufficient and necessary causes, we employ a classical concept, the probability of sufficiency and necessary causes (PNS), which indicates the probability of whether one is the necessary and sufficient cause.
no code implementations • 5 Aug 2023 • Jiarui Jin, Xianyu Chen, Weinan Zhang, Mengyue Yang, Yang Wang, Yali Du, Yong Yu, Jun Wang
Notice that these ranking metrics do not consider the effects of the contextual dependence among the items in the list, we design a new family of simulation-based ranking metrics, where existing metrics can be regarded as special cases.
1 code implementation • NeurIPS 2023 • Xidong Feng, Yicheng Luo, Ziyan Wang, Hongrui Tang, Mengyue Yang, Kun Shao, David Mguni, Yali Du, Jun Wang
Thus, we propose ChessGPT, a GPT model bridging policy learning and language modeling by integrating data from these two sources in Chess games.
no code implementations • 17 Feb 2022 • Mengyue Yang, Xinyu Cai, Furui Liu, Xu Chen, Zhitang Chen, Jianye Hao, Jun Wang
It is evidence that representation learning can improve model's performance over multiple downstream tasks in many real-world scenarios, such as image classification and recommender systems.
no code implementations • 16 Jan 2022 • Mengyue Yang, Guohao Cai, Furui Liu, Zhenhua Dong, Xiuqiang He, Jianye Hao, Jun Wang, Xu Chen
To alleviate these problems, in this paper, we propose a novel debiased recommendation framework based on user feature balancing.
no code implementations • 29 Sep 2021 • Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jianye Hao, Jun Wang
In many real-world scenarios, such as image classification and recommender systems, it is evidence that representation learning can improve model's performance over multiple downstream tasks.
no code implementations • 2 Sep 2021 • Mengyue Yang, Quanyu Dai, Zhenhua Dong, Xu Chen, Xiuqiang He, Jun Wang
To alleviate this problem, in this paper, we propose to reformulate the recommendation task within the causal inference framework, which enables us to counterfactually simulate user ranking-based preferences to handle the data scarce problem.
no code implementations • 28 Dec 2020 • Minne Li, Mengyue Yang, Furui Liu, Xu Chen, Zhitang Chen, Jun Wang
The capability of imagining internally with a mental model of the world is vitally important for human cognition.
2 code implementations • CVPR 2021 • Mengyue Yang, Furui Liu, Zhitang Chen, Xinwei Shen, Jianye Hao, Jun Wang
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data.
1 code implementation • 2 Apr 2020 • Mengyue Yang, Qingyang Li, Zhiwei Qin, Jieping Ye
In this paper, we propose a hierarchical adaptive contextual bandit method (HATCH) to conduct the policy learning of contextual bandits with a budget constraint.