no code implementations • 7 Jun 2023 • Xianyu Chen, Jian Shen, Wei Xia, Jiarui Jin, Yakun Song, Weinan Zhang, Weiwen Liu, Menghui Zhu, Ruiming Tang, Kai Dong, Dingyin Xia, Yong Yu
Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm.
no code implementations • NeurIPS 2021 • Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu
However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.
1 code implementation • 19 Nov 2021 • Jianfeng Chi, Jian Shen, Xinyi Dai, Weinan Zhang, Yuan Tian, Han Zhao
We first provide a decomposition theorem for return disparity, which decomposes the return disparity of any two MDPs sharing the same state and action spaces into the distance between group-wise reward functions, the discrepancy of group policies, and the discrepancy between state visitation distributions induced by the group policies.
1 code implementation • NeurIPS 2021 • Hang Lai, Jian Shen, Weinan Zhang, Yimin Huang, Xing Zhang, Ruiming Tang, Yong Yu, Zhenguo Li
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency.
1 code implementation • 3 Nov 2021 • Minghuan Liu, Hanye Zhao, Zhengyu Yang, Jian Shen, Weinan Zhang, Li Zhao, Tie-Yan Liu
However, IL is usually limited in the capability of the behavioral policy and tends to learn a mediocre behavior from the dataset collected by the mixture of policies.
no code implementations • NeurIPS 2021 • Minghuan Liu, Zhengbang Zhu, Yuzheng Zhuang, Weinan Zhang, Jian Shen, Jianye Hao, Yong Yu, Jun Wang
State-only imitation learning (SOIL) enables agents to learn from massive demonstrations without explicit action or reward information.
1 code implementation • 16 Aug 2021 • Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao, Weinan Zhang, Xiawei Guo, Jian Shen, Yanru Qu, Jieli Lu, Min Xu, Yu Xu, Tiange Wang, Mian Li, Wei-Wei Tu, Yong Yu, Yufang Bi, Weiqing Wang, Guang Ning
To tackle the above challenges, we employ gradient boosting decision trees (GBDT) to handle data heterogeneity and introduce multi-task learning (MTL) to solve data insufficiency.
1 code implementation • 13 May 2021 • Menghui Zhu, Minghuan Liu, Jian Shen, Zhicheng Zhang, Sheng Chen, Weinan Zhang, Deheng Ye, Yong Yu, Qiang Fu, Wei Yang
In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem.
1 code implementation • 7 May 2021 • Weinan Zhang, Xihuai Wang, Jian Shen, Ming Zhou
We specify the dynamics sample complexity and the opponent sample complexity in MARL, and conduct a theoretic analysis of return discrepancy upper bound.
Multi-agent Reinforcement Learning Reinforcement Learning (RL)
1 code implementation • 9 Dec 2020 • Yunfei Liu, Yang Yang, Xianyu Chen, Jian Shen, Haifeng Zhang, Yong Yu
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response.
Ranked #3 on Knowledge Tracing on EdNet
1 code implementation • NeurIPS 2020 • Jian Shen, Han Zhao, Weinan Zhang, Yong Yu
However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance.
3 code implementations • 13 Sep 2020 • Yang Yang, Jian Shen, Yanru Qu, Yunfei Liu, Kerong Wang, Yaoming Zhu, Wei-Nan Zhang, Yong Yu
With the rapid development in online education, knowledge tracing (KT) has become a fundamental problem which traces students' knowledge status and predicts their performance on new questions.
Ranked #7 on Knowledge Tracing on EdNet
1 code implementation • ICML 2020 • Hang Lai, Jian Shen, Wei-Nan Zhang, Yong Yu
Model-based reinforcement learning approaches leverage a forward dynamics model to support planning and decision making, which, however, may fail catastrophically if the model is inaccurate.
no code implementations • 14 Mar 2020 • Guansong Lu, Zhiming Zhou, Jian Shen, Cheng Chen, Wei-Nan Zhang, Yong Yu
Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning.
no code implementations • 21 Nov 2019 • Yuxuan Song, Lantao Yu, Zhangjie Cao, Zhiming Zhou, Jian Shen, Shuo Shao, Wei-Nan Zhang, Yong Yu
Domain adaptation aims to leverage the supervision signal of source domain to obtain an accurate model for target domain, where the labels are not available.
1 code implementation • 2 Apr 2019 • Zhiming Zhou, Jian Shen, Yuxuan Song, Wei-Nan Zhang, Yong Yu
Lipschitz continuity recently becomes popular in generative adversarial networks (GANs).
no code implementations • NAACL 2018 • Zhenghui Wang, Yanru Qu, Li-Heng Chen, Jian Shen, Wei-Nan Zhang, Shaodian Zhang, Yimei Gao, Gen Gu, Ken Chen, Yong Yu
We study the problem of named entity recognition (NER) from electronic medical records, which is one of the most fundamental and critical problems for medical text mining.
Medical Named Entity Recognition named-entity-recognition +3
8 code implementations • 5 Jul 2017 • Jian Shen, Yanru Qu, Wei-Nan Zhang, Yong Yu
Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL).