7 papers
Node4All: Learning Node Representation Beyond Datasets
Dooho Lee, Jaemin Yoo
Node representation learning has advanced rapidly, yet most existing methods rely on per-dataset training and hyperparameter tuning. This dataset-specific optimization comes from t…
Bounded Context Management for Tabular Foundation Models on Stream Learning
Jinmo Lee, Doyun Choi, Moongi Choi +1
Tabular stream learning requires predictions on sequentially arriving examples under distribution shift. While standard methods adapt by updating model states, tabular foundation m…
Generalizing Multi-Scale Time-Series Modeling with a Single Operator
Cheonwoo Lee, Dooho Lee, Doyun Choi +1
Multi-scale modeling has emerged as an effective design principle for time-series forecasting by capturing temporal dynamics at multiple resolutions. As no principled foundation ha…
Aggregation Buffer: Revisiting DropEdge with a New Parameter Block
Dooho Lee, Myeong Kong, Sagad Hamid +2
We revisit DropEdge, a data augmentation technique for GNNs which randomly removes edges to expose diverse graph structures during training. While being a promising approach to eff…
PULSE: Socially-Aware User Representation Modeling Toward Parameter-Efficient Graph Collaborative Filtering
Doyun Choi, Cheonwoo Lee, Biniyam Aschalew Tolera +3
Graph-based social recommendation (SocialRec) has emerged as a powerful extension of graph collaborative filtering (GCF), which leverages graph neural networks (GNNs) to capture mu…
Simple and Behavior-Driven Augmentation for Recommendation with Rich Collaborative Signals
Doyun Choi, Cheonwoo Lee, Jaemin Yoo
Contrastive learning (CL) has been widely used for enhancing the performance of graph collaborative filtering (GCF) for personalized recommendation. Since data augmentation plays a…