3 papers
cs.LG2026
Effective Dataset Distillation for Spatio-Temporal Forecasting with Bi-dimensional Compression
Taehyung Kwon, Yeonje Choi, Yeongho Kim +1
Spatio-temporal time series are widely used in real-world applications, including traffic prediction and weather forecasting. They are sequences of observations over extensive peri…
cs.LG2025
Simple yet Effective Node Property Prediction on Edge Streams under Distribution Shifts
Jongha Lee, Taehyung Kwon, Heechan Moon +1
The problem of predicting node properties (e.g., node classes) in graphs has received significant attention due to its broad range of applications. Graphs from real-world datasets…
cs.LG2024
BeGin: Extensive Benchmark Scenarios and An Easy-to-use Framework for Graph Continual Learning
Jihoon Ko, Shinhwan Kang, Taehyung Kwon +2
Continual Learning (CL) is the process of learning ceaselessly a sequence of tasks. Most existing CL methods deal with independent data (e.g., images and text) for which many bench…