2024-11-25-lin24a.md

title	abstract	openreview	layout	series	publisher	issn	id	month	tex_title	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
A LUPI distillation-based approach: Application to predicting Proximal Junctional Kyphosis	We propose a learning algorithm called XGBoost+, a modified version of the extreme gradient boosting algorithm (XGBoost). The new algorithm utilizes privileged information (PI), data collected after inference time. XGBoost+ incorporates PI into a distillation framework for XGBoost. We also evaluate our proposed method on a real-world clinical dataset about Proximal Junctional Kyphosis (PJK). Our approach outperforms vanilla XGBoost, SVM, and SVM+ on various datasets. Our approach showcases the advantage of using privileged information to improve the performance of machine learning models in healthcare, where data after inference time can be leveraged to build better models.	LvEdt6YqbT	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	lin24a	0	A {LUPI} distillation-based approach: Application to predicting Proximal Junctional Kyphosis	false	Lin, Yun Chao and Clark-Sevilla, Andrea and Ravindranath, Rohith and Hassan, Fthimnir and Reyes, Justin and Lombardi, Joseph and Lenke, Lawrence G. and Salleb-Aouissi, Ansaf	given family Yun Chao Lin given family Andrea Clark-Sevilla given family Rohith Ravindranath given family Fthimnir Hassan given family Justin Reyes given family Joseph Lombardi given family Lawrence G. Lenke given family Ansaf Salleb-Aouissi	2024-11-25		Proceedings of the 9th Machine Learning for Healthcare Conference	252	inproceedings	date-parts 2024 11 25	https://raw.githubusercontent.com/mlresearch/v252/main/assets/lin24a/lin24a.pdf