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 22 papers between 2017 and 2024.

Collaborative distances:

Dijkstra number² of four.
Erdős number³ of four.

Timeline

Legend:

Book

In proceedings

Article

PhD thesis

Dataset

Other

Bibliography

2024

Counterfactual contextual bandit for recommendation under delayed feedback.

[BibT_eX]

[DOI]

Neural Comput. Appl., August, 2024

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

[BibT_eX]

[DOI]

Neural Networks, 2024

Causal-learn: Causal Discovery in Python.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2024

Individual Causal Structure Learning from Population Data.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, 2024

Expensive Constrained Multi-objective Evolutionary Algorithm with Pareto Set Learning.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

Identification of Causal Structure with Latent Variables Based on Higher Order Cumulants.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023

Learning dynamic causal mechanisms from non-stationary data.

[BibT_eX]

[DOI]

Appl. Intell., March, 2023

Latent Causal Dynamics Model for Model-Based Reinforcement Learning.

[BibT_eX]

[DOI]

Proceedings of the Neural Information Processing - 30th International Conference, 2023

Causal Discovery with Latent Confounders Based on Higher-Order Cumulants.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning, 2023

2022

Causal Discovery in Linear Non-Gaussian Acyclic Model With Multiple Latent Confounders.

[BibT_eX]

[DOI]

IEEE Trans. Neural Networks Learn. Syst., 2022

A Latent Variable Augmentation Method for Image Categorization with Insufficient Training Samples.

[BibT_eX]

[DOI]

ACM Trans. Knowl. Discov. Data, 2022

Learning granger causality for non-stationary Hawkes processes.

[BibT_eX]

[DOI]

Neurocomputing, 2022

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

[BibT_eX]

[DOI]

Neurocomputing, 2022

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

[BibT_eX]

[DOI]

CoRR, 2022

REST: Debiased Social Recommendation via Reconstructing Exposure Strategies.

[BibT_eX]

[DOI]

CoRR, 2022

Causal Alignment Based Fault Root Causes Localization for Wireless Network.

[BibT_eX]

[DOI]

Proceedings of the IEEE International Conference on Acoustics, 2022

2021

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

[BibT_eX]

[DOI]

CoRR, 2021

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

[BibT_eX]

[DOI]

CoRR, 2021

Time Series Domain Adaptation via Sparse Associative Structure Alignment.

[BibT_eX]

[DOI]

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Mining hidden non-redundant causal relationships in online social networks.

[BibT_eX]

[DOI]

Neural Comput. Appl., 2020

2017

An efficient kurtosis-based causal discovery method for linear non-Gaussian acyclic data.

[BibT_eX]

[DOI]

Proceedings of the 25th IEEE/ACM International Symposium on Quality of Service, 2017

Wei Chen

Timeline

Legend:

Links

Online presence:

On csauthors.net:

Bibliography

Loading...