Wei Chen

Orcid: 0000-0002-8213-0567

Affiliations:
  • Guangdong University of Technology, School of Computer Science, Guangzhou, China


According to our database1, Wei Chen authored at least 25 papers between 2017 and 2025.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2025
Causal-aware Large Language Models: Enhancing Decision-Making Through Learning, Adapting and Acting.
CoRR, May, 2025

Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption.
CoRR, February, 2025

Temporal latent variable structural causal model for causal discovery under external interferences.
Neurocomputing, 2025

2024
Counterfactual contextual bandit for recommendation under delayed feedback.
Neural Comput. Appl., August, 2024

Time-series domain adaptation via sparse associative structure alignment: Learning invariance and variance.
Neural Networks, 2024

Causal-learn: Causal Discovery in Python.
J. Mach. Learn. Res., 2024

Individual Causal Structure Learning from Population Data.
Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Expensive Constrained Multi-objective Evolutionary Algorithm with Pareto Set Learning.
Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2024

TNPAR: Topological Neural Poisson Auto-Regressive Model for Learning Granger Causal Structure from Event Sequences.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
Learning dynamic causal mechanisms from non-stationary data.
Appl. Intell., March, 2023

Latent Causal Dynamics Model for Model-Based Reinforcement Learning.
Proceedings of the Neural Information Processing - 30th International Conference, 2023

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants.
Proceedings of the International Conference on Machine Learning, 2023

2022
Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.
IEEE Trans. Neural Networks Learn. Syst., 2022

A Latent Variable Augmentation Method for Image Categorization with Insufficient Training Samples.
ACM Trans. Knowl. Discov. Data, 2022

Learning granger causality for non-stationary Hawkes processes.
Neurocomputing, 2022

Shared state space model for background information extraction and time series prediction.
Neurocomputing, 2022

Time-Series Domain Adaptation via Sparse Associative Structure Alignment: Learning Invariance and Variance.
CoRR, 2022

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies.
CoRR, 2022

Causal Alignment Based Fault Root Causes Localization for Wireless Network.
Proceedings of the IEEE International Conference on Acoustics, 2022

2021
CCSL: A Causal Structure Learning Method from Multiple Unknown Environments.
CoRR, 2021

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders.
CoRR, 2021

Time Series Domain Adaptation via Sparse Associative Structure Alignment.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020
Mining hidden non-redundant causal relationships in online social networks.
Neural Comput. Appl., 2020

2017
An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data.
Proceedings of the 25th IEEE/ACM International Symposium on Quality of Service, 2017


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