3 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
TabLoRA: Parameter-Efficient Low-Rank Ensemble Learning for Large-Scale Tabular Data
Jiaqi Luo, Shixin Xu
Tabular learning is still dominated by gradient-boosted decision trees (GBDTs), while recent deep learning approaches have become increasingly competitive. However, applying deep t…
Efficient Global-Local Fusion Sampling for Physics-Informed Neural Networks
Jiaqi Luo, Shixin Xu, Zhouwang Yang
The accuracy of Physics-Informed Neural Networks (PINNs) critically depends on the placement of collocation points, as the PDE loss is approximated through sampling over the soluti…
An Imbalanced Learning-based Sampling Method for Physics-informed Neural Networks
Jiaqi Luo, Yahong Yang, Yuan Yuan +2
This paper introduces Residual-based Smote (RSmote), an innovative local adaptive sampling technique tailored to improve the performance of Physics-Informed Neural Networks (PINNs)…
Improving GBDT Performance on Imbalanced Datasets: An Empirical Study of Class-Balanced Loss Functions
Jiaqi Luo, Yuan Yuan, Shixin Xu
Class imbalance remains a significant challenge in machine learning, particularly for tabular data classification tasks. While Gradient Boosting Decision Trees (GBDT) models have p…
Robust-GBDT: GBDT with Nonconvex Loss for Tabular Classification in the Presence of Label Noise and Class Imbalance
Jiaqi Luo, Yuedong Quan, Shixin Xu
Dealing with label noise in tabular classification tasks poses a persistent challenge in machine learning. While robust boosting methods have shown promise in binary classification…