Siwen Yan: “Efficient Learning of Fair Models using Human Guidance and Privileged Information”

My recent research is centered around developing tree-based ensemble models and utilizing large language models. To enhance the performance and fairness of our models at deployment, we leverage privileged information as guidance in XGBoost and incorporate human advice into relational regression trees using functional gradient boosting for more effective and efficient learning. Additionally, we employ LLMs to discover logical rules and generate missing data. In my previous works, I have focused on various areas including Statistical Relational AI and Healthcare, Graph Neural Networks, and Reinforcement Learning.