Wen Huang Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

Researcher

University of Arkansas at Fayetteville

faculty

8 h-index 29 pubs 328 cited

Research Areas

Fairness-Aware Machine Learning Causal Inference in Machine Learning Bandit Algorithms Recommendation Systems Machine Learning Model Robustness Decision-Making and Behavioral Economics

Links

ORCID Lab Website

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OverviewAI-generated summary

Wen Huang's research focuses on developing robust and fair machine learning algorithms, particularly in the context of bandit algorithms and recommendation systems. His work addresses challenges such as selection bias and confounding in offline data, aiming to improve algorithm performance and fairness for users. Huang has investigated methods for achieving counterfactual fairness and user-side fairness in contextual bandit settings.

His publications include work on learning identifiable causal representations using structural knowledge and developing robust classifiers under missing-not-at-random sample selection bias. Huang has a body of work in fairness-aware machine learning, with recent publications in 2023 and 2024. He has collaborated with researchers at the University of Arkansas at Fayetteville, including Xintao Wu, Kevin Labille, and Huy Mai. Huang's scholarly output includes 29 publications, with 328 citations and an h-index of 8.

Metrics

  • h-index: 8
  • Publications: 29
  • Citations: 328

Selected Publications

  • Robustly Improving Bandit Algorithms with Confounded and Selection Biased Offline Data: A Causal Approach (2024) DOI
  • Mitigating Confounding and Selection Biases in Personalized Recommendation: A Causal Approach (2023) DOI
  • A Robust Classifier under Missing-Not-at-Random Sample Selection Bias (2023) DOI
  • SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge (2022) DOI
  • Achieving Counterfactual Fairness for Causal Bandit (2022) DOI
  • Achieving User-Side Fairness in Contextual Bandits (2022) DOI
  • Fairness-aware Bandit-based Recommendation (2021) DOI
  • Transferable Contextual Bandits with Prior Observations (2021) DOI

Collaborators

Researchers in the database who share publications

Xintao Wu University of Arkansas at Fayetteville 5 shared publications Kevin Labille University of Arkansas at Fayetteville 3 shared publications Huy Mai University of Arkansas at Fayetteville 1 shared publication