Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning

AAAI 2025 pp. 29317-29318

doi:10.1609/AAAI.V39I28.35236 /aaai/2025/zhang2025aaai-statistical/

Abstract

While advances in machine learning and the expansion of massive datasets have significantly improved predictive accuracy, the translation of these predictions into actionable decisions—alongside a robust understanding of associated risks—remains underexplored. My research focuses on developing methodology and theory in data-driven decision-making and uncertainty quantification that effectively address core data challenges. This paper presents two connected pillars of my research: data-driven contextual optimization, uncertainty quantification and reduction.

AAAI Semantic Scholar

Cite

Text

Zhang. "Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning." AAAI Conference on Artificial Intelligence, 2025. doi:10.1609/AAAI.V39I28.35236

Markdown

[Zhang. "Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning." AAAI Conference on Artificial Intelligence, 2025.](https://mlanthology.org/aaai/2025/zhang2025aaai-statistical/) doi:10.1609/AAAI.V39I28.35236

BibTeX

@inproceedings{zhang2025aaai-statistical,
  title     = {{Statistical Methodologies for Decision-Making and Uncertainty Reduction in Machine Learning}},
  author    = {Zhang, Haofeng},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2025},
  pages     = {29317-29318},
  doi       = {10.1609/AAAI.V39I28.35236},
  url       = {https://mlanthology.org/aaai/2025/zhang2025aaai-statistical/}
}