91 citations · 153 across the 9 of their papers we have counts for
9 papers
MERIT Feedback Elicits Better Bargaining in LLM Negotiators
Jihwan Oh, Murad Aghazada, Yooju Shin +2
Bargaining is often regarded as a logical arena rather than an art or a matter of intuition, yet Large Language Models (LLMs) still struggle to navigate it due to limited strategic…
VarDrop: Enhancing Training Efficiency by Reducing Variate Redundancy in Periodic Time Series Forecasting
Junhyeok Kang, Yooju Shin, Jae-Gil Lee
Variate tokenization, which independently embeds each variate as separate tokens, has achieved remarkable improvements in multivariate time series forecasting. However, employing s…
Universal Time-Series Representation Learning: A Survey
Patara Trirat, Yooju Shin, Junhyeok Kang +6
Time-series data exists in every corner of real-world systems and services, ranging from satellites in the sky to wearable devices on human bodies. Learning representations by extr…
Adaptive Shortcut Debiasing for Online Continual Learning
Doyoung Kim, Dongmin Park, Yooju Shin +3
We propose a novel framework DropTop that suppresses the shortcut bias in online continual learning (OCL) while being adaptive to the varying degree of the shortcut bias incurred b…
Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Dongmin Park, Yooju Shin, Jihwan Bang +3
Unlabeled data examples awaiting annotations contain open-set noise inevitably. A few active learning studies have attempted to deal with this open-set noise for sample selection b…
Meta-Learning for Online Update of Recommender Systems
Minseok Kim, Hwanjun Song, Yooju Shin +3
Online recommender systems should be always aligned with users' current interest to accurately suggest items that each user would like. Since user interest usually evolves over tim…
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
Jin-woo Lee, Jaehoon Oh, Yooju Shin +2
Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distribute…
Robust Learning by Self-Transition for Handling Noisy Labels
Hwanjun Song, Minseok Kim, Dongmin Park +2
Real-world data inevitably contains noisy labels, which induce the poor generalization of deep neural networks. It is known that the network typically begins to rapidly memorize fa…
Learning from Noisy Labels with Deep Neural Networks: A Survey
Hwanjun Song, Minseok Kim, Dongmin Park +2
Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of…