Wen Huang
This is a likely match — the affiliation was inferred from OpenAlex, ORCID, and web sources but has not been fully confirmed. Treat with appropriate caution.
Researcher
Also affiliated: Xidian University (2017); Sun Yat-sen University (2010); Xiamen University (2020); Kuang-Chi (China) (2017); The First Affiliated Hospital, Sun Yat-sen University (2010); Huazhong University of Science and Technology (2003–2012); China Southern Power Grid (China) (2016); Beijing Information Science & Technology University (2012–2022)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Wen Huang's research focuses on developing and applying causal inference and machine learning techniques to address complex data challenges. Recent work includes developing methods for achieving fairness in contextual bandits, a type of reinforcement learning algorithm used in recommendation systems and decision-making processes. Huang has also investigated robust classification methods designed to handle sample selection bias, particularly in scenarios where data may be missing not at random. This work aims to improve the reliability and fairness of machine learning models when applied to real-world datasets, which often contain inherent biases and complexities. Collaborations with researchers at the University of Arkansas at Fayetteville have contributed to a publication record of 29 works, with an h-index of 9 and 344 total citations.
Metrics
- h-index: 9
- Publications: 29
- Citations: 348
Selected Publications
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Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach (2024)
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Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach (2023)
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A Robust Classifier under Missing-Not-at-Random Sample Selection Bias (2023)
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SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge (2022)
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Achieving Counterfactual Fairness for Causal Bandit (2022)
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Achieving User-Side Fairness in Contextual Bandits (2022)
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Fairness-aware Bandit-based Recommendation (2021)
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Transferable Contextual Bandits with Prior Observations (2021)
Collaboration Network
Top Collaborators
- Achieving Counterfactual Fairness for Causal Bandit
- Achieving User-Side Fairness in Contextual Bandits
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- Fairness-aware Bandit-based Recommendation
- Transferable Contextual Bandits with Prior Observations
Showing 5 of 8 shared publications
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Transferable Contextual Bandits with Prior Observations
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Achieving User-Side Fairness in Contextual Bandits
- Fairness-aware Bandit-based Recommendation
- Achieving Counterfactual Fairness for Causal Bandit
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge
- A Robust Classifier under Missing-Not-at-Random Sample Selection Bias
- A Robust Classifier under Missing-Not-at-Random Sample Selection Bias
- Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach
- Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach
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