57 citations · 144 across the 21 of their papers we have counts for
8 papers · 1 filter
ConfounderGAN: Protecting Image Data Privacy with Causal Confounder
Qi Tian, Kun Kuang, Kelu Jiang +3
The success of deep learning is partly attributed to the availability of massive data downloaded freely from the Internet. However, it also means that users' private data may be co…
RepMode: Learning to Re-parameterize Diverse Experts for Subcellular Structure Prediction
Donghao Zhou, Chunbin Gu, Junde Xu +4
In biological research, fluorescence staining is a key technique to reveal the locations and morphology of subcellular structures. However, it is slow, expensive, and harmful to ce…
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Furui Liu +1
Offline multi-agent reinforcement learning (MARL) aims to learn effective multi-agent policies from pre-collected datasets, which is an important step toward the deployment of mult…
Learning Instrumental Variable from Data Fusion for Treatment Effect Estimation
Anpeng Wu, Kun Kuang, Ruoxuan Xiong +6
The advent of the big data era brought new opportunities and challenges to draw treatment effect in data fusion, that is, a mixed dataset collected from multiple sources (each sour…
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning
Zeren Huang, Wenhao Chen, Weinan Zhang +7
Deriving a good variable selection strategy in branch-and-bound is essential for the efficiency of modern mixed-integer programming (MIP) solvers. With MIP branching data collected…
S2RL: Do We Really Need to Perceive All States in Deep Multi-Agent Reinforcement Learning?
Shuang Luo, Yinchuan Li, Jiahui Li +4
Collaborative multi-agent reinforcement learning (MARL) has been widely used in many practical applications, where each agent makes a decision based on its own observation. Most ma…
Generalizable Information Theoretic Causal Representation
Mengyue Yang, Xinyu Cai, Furui Liu +4
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 recommende…
Debiased Recommendation with User Feature Balancing
Mengyue Yang, Guohao Cai, Furui Liu +5
Debiased recommendation has recently attracted increasing attention from both industry and academic communities. Traditional models mostly rely on the inverse propensity score (IPS…