41 citations · 41 across the 4 of their papers we have counts for
4 papers
DAMEL: Dual-Axis Multi-Expert Learning for Class-Imbalanced Learning
Hyuck Lee, Taemin Park, Heeyoung Kim
Various algorithms have been proposed to address the challenges posed by class-imbalanced learning from real-world data with long-tailed distributions. While these algorithms reduc…
Revisiting LLMs as Zero-Shot Time-Series Forecasters: Small Noise Can Break Large Models
Junwoo Park, Hyuck Lee, Dohyun Lee +2
Large Language Models (LLMs) have shown remarkable performance across diverse tasks without domain-specific training, fueling interest in their potential for time-series forecastin…
CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning
Hyuck Lee, Heeyoung Kim
Pseudo-label-based semi-supervised learning (SSL) algorithms trained on a class-imbalanced set face two cascading challenges: 1) Classifiers tend to be biased towards majority clas…
ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning
Hyuck Lee, Seungjae Shin, Heeyoung Kim
Existing semi-supervised learning (SSL) algorithms typically assume class-balanced datasets, although the class distributions of many real-world datasets are imbalanced. In general…