most citedAlleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation

63 citations · 65 across the 3 of their papers we have counts for

collaborators

3 papers

cs.IR2023★ 63 cited

Alleviating Matthew Effect of Offline Reinforcement Learning in Interactive Recommendation

Chongming Gao, Kexin Huang, Jiawei Chen +6

Offline reinforcement learning (RL), a technology that offline learns a policy from logged data without the need to interact with online environments, has become a favorable choice…

cs.DB2023

Learn to Explore: on Bootstrapping Interactive Data Exploration with Meta-learning

Yukun Cao, Xike Xie, Kexin Huang

Interactive data exploration (IDE) is an effective way of comprehending big data, whose volume and complexity are beyond human abilities. The main goal of IDE is to discover user i…

cs.LG2021★ 2 cited

Machine Learning Applications for Therapeutic Tasks with Genomics Data

Kexin Huang, Cao Xiao, Lucas M. Glass +3

Thanks to the increasing availability of genomics and other biomedical data, many machine learning approaches have been proposed for a wide range of therapeutic discovery and devel…