17 citations · 65 across the 12 of their papers we have counts for
12 papers
Towards Return Parity in Markov Decision Processes
Jianfeng Chi, Jian Shen, Xinyi Dai +3
Algorithmic decisions made by machine learning models in high-stakes domains may have lasting impacts over time. However, naive applications of standard fairness criterion in stati…
On Effective Scheduling of Model-based Reinforcement Learning
Hang Lai, Jian Shen, Weinan Zhang +5
Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency. Despite its impressive success so far, it is still unclear how to appropriate…
Curriculum Offline Imitation Learning
Minghuan Liu, Hanye Zhao, Zhengyu Yang +4
Offline reinforcement learning (RL) tasks require the agent to learn from a pre-collected dataset with no further interactions with the environment. Despite the potential to surpas…
Task-wise Split Gradient Boosting Trees for Multi-center Diabetes Prediction
Mingcheng Chen, Zhenghui Wang, Zhiyun Zhao +14
Diabetes prediction is an important data science application in the social healthcare domain. There exist two main challenges in the diabetes prediction task: data heterogeneity si…
MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks
Menghui Zhu, Minghuan Liu, Jian Shen +7
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. In…
Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts
Weinan Zhang, Xihuai Wang, Jian Shen +1
This paper investigates the model-based methods in multi-agent reinforcement learning (MARL). We specify the dynamics sample complexity and the opponent sample complexity in MARL,…
Improving Knowledge Tracing via Pre-training Question Embeddings
Yunfei Liu, Yang Yang, Xianyu Chen +3
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to…
Model-based Policy Optimization with Unsupervised Model Adaptation
Jian Shen, Han Zhao, Weinan Zhang +1
Model-based reinforcement learning methods learn a dynamics model with real data sampled from the environment and leverage it to generate simulated data to derive an agent. However…
GIKT: A Graph-based Interaction Model for Knowledge Tracing
Yang Yang, Jian Shen, Yanru Qu +5
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 ne…
Bidirectional Model-based Policy Optimization
Hang Lai, Jian Shen, Weinan Zhang +1
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 i…
Large-Scale Optimal Transport via Adversarial Training with Cycle-Consistency
Guansong Lu, Zhiming Zhou, Jian Shen +3
Recent advances in large-scale optimal transport have greatly extended its application scenarios in machine learning. However, existing methods either not explicitly learn the tran…
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Yuxuan Song, Lantao Yu, Zhangjie Cao +5
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. To leverage and adapt…