Yaowei Hu (胡耀炜)

Senior Data Scientist

Walmart Inc.

Member of the People.AI team

About Me: I am a senior data scientist in Walmart Inc. Previously, I got my Ph.D. from the EECS department at University of Arkansas, advised by Prof. Lu Zhang. And I obtained my M.S. and B.ENG. from the college of computer at Sichuan University in China.

Research Interest: Currently, I have been working on retrieval augmented generation (RAG) for LLMs. During my PhD, my research primarily focused on fair machine learning and causal inference. Specifically, I proposed various fairness algorithms to achieve long-term fairness in sequential decision-making. My interests also include generative models, reinforcement learning, natural language processing.

News

Dec 9, 2023	Our paper “Long-term Fair Decision Making Through Deep Generative Models” has been accepted by AAAI 2024.
Oct 28, 2023	Our paper “Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning” has been accepted by IEEE Big Data 2023 as a regular paper.
Jun 22, 2023	Our paper “Long-term Fair Decision Making Through Deep Generative Models” has been accepted at 2nd SIGKDD Workshop on Ethical Artifical Intelligence: Methods and Applications.

Selected publications

AAAI

Long-term Fair Decision Making Through Deep Generative Models

Yaowei Hu, Yongkai Wu, and Lu Zhang

In Proceedings of the AAAI Conference on Artificial Intelligence, 2024

Bib PDF Supp Code Poster

@inproceedings{hu2024long,
  title = {Long-term Fair Decision Making Through Deep Generative Models},
  author = {Hu, Yaowei and Wu, Yongkai and Zhang, Lu},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {38},
  number = {20},
  pages = {22114-22122},
  year = {2024},
}

Dissertation

Towards Long-term Fairness in Sequential Decision Making

Yaowei Hu

In University of Arkansas, 2023

Bib PDF

@inproceedings{hu2023towards,
  title = {Towards Long-term Fairness in Sequential Decision Making},
  author = {Hu, Yaowei},
  booktitle = {University of Arkansas},
  year = {2023},
}

Big Data

Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning

Yaowei Hu, Jacob Lear, and Lu Zhang

In IEEE International Conference on Big Data (BigData), 2023

Bib PDF Code

@inproceedings{hu2023striking,
  title = {Striking a Balance in Fairness for Dynamic Systems Through Reinforcement Learning},
  author = {Hu, Yaowei and Lear, Jacob and Zhang, Lu},
  booktitle = {IEEE International Conference on Big Data (BigData)},
  year = {2023},
  pages = {662--671},
}

AAAIOral

Achieving Long-term Fairness in Sequential Decision Making

Yaowei Hu, and Lu Zhang

In Proceedings of the AAAI Conference on Artificial Intelligence, 2022

Bib PDF Supp Code Poster Slides

@inproceedings{hu2022achieving,
  title = {Achieving Long-term Fairness in Sequential Decision Making},
  author = {Hu, Yaowei and Zhang, Lu},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {36},
  number = {9},
  pages = {9549--9557},
  year = {2022},
}

AAAI

A Generative Adversarial Framework for Bounding Confounded Causal Effects

Yaowei Hu, Yongkai Wu, Lu Zhang, and 1 more author

In Proceedings of the AAAI Conference on Artificial Intelligence, 2021

Bib PDF Supp Code Poster Slides

@inproceedings{hu2021generative,
  title = {A Generative Adversarial Framework for Bounding Confounded Causal Effects},
  author = {Hu, Yaowei and Wu, Yongkai and Zhang, Lu and Wu, Xintao},
  booktitle = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume = {35},
  number = {13},
  pages = {12104--12112},
  year = {2021},
}

NeurIPS

Fair Multiple Decision Making Through Soft Interventions

Yaowei Hu, Yongkai Wu, Lu Zhang, and 1 more author

Advances in Neural Information Processing Systems, 2020

Bib PDF Supp Code Poster Slides

@article{hu2020fair,
  title = {Fair Multiple Decision Making Through Soft Interventions},
  author = {Hu, Yaowei and Wu, Yongkai and Zhang, Lu and Wu, Xintao},
  journal = {Advances in Neural Information Processing Systems},
  volume = {33},
  pages = {17965--17975},
  year = {2020},
}