5 citations · 9 across the 21 of their papers we have counts for
3 papers · 1 filter
Filling the Gaps: Selective Knowledge Augmentation for LLM Recommenders
Jaehyun Lee, Sanghwan Jang, SeongKu Kang +1
Large language models (LLMs) have recently emerged as powerful training-free recommenders. However, their knowledge of individual items is inevitably uneven due to imbalanced infor…
Dynamic Multi-period Experts for Online Time Series Forecasting
Seungha Hong, Sukang Chae, Suyeon Kim +2
Online Time Series Forecasting (OTSF) requires models to continuously adapt to concept drift. However, existing methods often treat concept drift as a monolithic phenomenon. To add…
Harmonic Dataset Distillation for Time Series Forecasting
Seungha Hong, Sanghwan Jang, Wonbin Kweon +3
Time Series forecasting (TSF) in the modern era faces significant computational and storage cost challenges due to the massive scale of real-world data. Dataset Distillation (DD),…