57 citations · 144 across the 21 of their papers we have counts for
22 papers
Specify Robust Causal Representation from Mixed Observations
Mengyue Yang, Xinyu Cai, Furui Liu +2
Learning representations purely from observations concerns the problem of learning a low-dimensional, compact representation which is beneficial to prediction models. Under the hyp…
Meta Adaptive Task Sampling for Few-Domain Generalization
Zheyan Shen, Han Yu, Peng Cui +4
To ensure the out-of-distribution (OOD) generalization performance, traditional domain generalization (DG) methods resort to training on data from multiple sources with different u…
Traj-MAE: Masked Autoencoders for Trajectory Prediction
Hao Chen, Jiaze Wang, Kun Shao +5
Trajectory prediction has been a crucial task in building a reliable autonomous driving system by anticipating possible dangers. One key issue is to generate consistent trajectory…
DR-Label: Improving GNN Models for Catalysis Systems by Label Deconstruction and Reconstruction
Bowen Wang, Chen Liang, Jiaze Wang +7
Attaining the equilibrium state of a catalyst-adsorbate system is key to fundamentally assessing its effective properties, such as adsorption energy. Machine learning methods with…
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…
CGIBNet: Bandwidth-constrained Communication with Graph Information Bottleneck in Multi-Agent Reinforcement Learning
Qi Tian, Kun Kuang, Baoxiang Wang +2
Communication is one of the core components for cooperative multi-agent reinforcement learning (MARL). The communication bandwidth, in many real applications, is always subject to…
Contrastive ACE: Domain Generalization Through Alignment of Causal Mechanisms
Yunqi Wang, Furui Liu, Zhitang Chen +4
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve t…
Shapley Counterfactual Credits for Multi-Agent Reinforcement Learning
Jiahui Li, Kun Kuang, Baoxiang Wang +4
Centralized Training with Decentralized Execution (CTDE) has been a popular paradigm in cooperative Multi-Agent Reinforcement Learning (MARL) settings and is widely used in many re…
Learning to Select Cuts for Efficient Mixed-Integer Programming
Zeren Huang, Kerong Wang, Furui Liu +6
Cutting plane methods play a significant role in modern solvers for tackling mixed-integer programming (MIP) problems. Proper selection of cuts would remove infeasible solutions in…
Causal World Models by Unsupervised Deconfounding of Physical Dynamics
Minne Li, Mengyue Yang, Furui Liu +3
The capability of imagining internally with a mental model of the world is vitally important for human cognition. If a machine intelligent agent can learn a world model to create a…
Weakly Supervised Disentangled Generative Causal Representation Learning
Xinwei Shen, Furui Liu, Hanze Dong +3
This paper proposes a Disentangled gEnerative cAusal Representation (DEAR) learning method under appropriate supervised information. Unlike existing disentanglement methods that en…
Decoder-free Robustness Disentanglement without (Additional) Supervision
Yifei Wang, Dan Peng, Furui Liu +3
Adversarial Training (AT) is proposed to alleviate the adversarial vulnerability of machine learning models by extracting only robust features from the input, which, however, inevi…
CausalVAE: Structured Causal Disentanglement in Variational Autoencoder
Mengyue Yang, Furui Liu, Zhitang Chen +3
Learning disentanglement aims at finding a low dimensional representation which consists of multiple explanatory and generative factors of the observational data. The framework of…
Confounder Detection in High Dimensional Linear Models using First Moments of Spectral Measures
Furui Liu, Laiwan Chan
In this paper, we study the confounder detection problem in the linear model, where the target variable is predicted using its potential causes . Based…
Causal Inference on Discrete Data via Estimating Distance Correlations
Furui Liu, Laiwan Chan
In this paper, we deal with the problem of inferring causal directions when the data is on discrete domain. By considering the distribution of the cause and the conditional…