38 citations · 79 across the 23 of their papers we have counts for
13 papers · 1 filter
LLM Agents for Bargaining with Utility-based Feedback
Jihwan Oh
Bargaining, a critical aspect of real-world interactions, presents challenges for large language models (LLMs) due to limitations in strategic depth and adaptation to complex human…
AdaSTaR: Adaptive Data Sampling for Training Self-Taught Reasoners
Woosung Koh, Wonbeen Oh, Jaein Jang +7
Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (…
: Scalable Auto-Feedback for LLM-based Chart Generation
Woosung Koh, Jang Han Yoon, MinHyung Lee +7
Generating high-quality charts with Large Language Models (LLMs) presents significant challenges due to limited data and the high cost of scaling through human curation. $\langle \…
Revisiting Early-Learning Regularization When Federated Learning Meets Noisy Labels
Taehyeon Kim, Donggyu Kim, Se-Young Yun
In the evolving landscape of federated learning (FL), addressing label noise presents unique challenges due to the decentralized and diverse nature of data collection across client…
Hypernetwork-Driven Model Fusion for Federated Domain Generalization
Marc Bartholet, Taehyeon Kim, Ami Beuret +2
Federated Learning (FL) faces significant challenges with domain shifts in heterogeneous data, degrading performance. Traditional domain generalization aims to learn domain-invaria…
Revisiting Architecture-aware Knowledge Distillation: Smaller Models and Faster Search
Taehyeon Kim, Heesoo Myeong, Se-Young Yun
Knowledge Distillation (KD) has recently emerged as a popular method for compressing neural networks. In recent studies, generalized distillation methods that find parameters and a…
Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction
Taehyeon Kim, Namgyu Ho, Donggyu Kim +1
Precipitation forecasting is an important scientific challenge that has wide-reaching impacts on society. Historically, this challenge has been tackled using numerical weather pred…
Supernet Training for Federated Image Classification under System Heterogeneity
Taehyeon Kim, Se-Young Yun
Efficient deployment of deep neural networks across many devices and resource constraints, particularly on edge devices, is one of the most challenging problems in the presence of…
Mold into a Graph: Efficient Bayesian Optimization over Mixed-Spaces
Jaeyeon Ahn, Taehyeon Kim, Seyoung Yun
Real-world optimization problems are generally not just black-box problems, but also involve mixed types of inputs in which discrete and continuous variables coexist. Such mixed-sp…
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Taehyeon Kim, Jaehoon Oh, NakYil Kim +2
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.…
FINE Samples for Learning with Noisy Labels
Taehyeon Kim, Jongwoo Ko, Sangwook Cho +2
Modern deep neural networks (DNNs) become frail when the datasets contain noisy (incorrect) class labels. Robust techniques in the presence of noisy labels can be categorized into…
Adaptive Local Bayesian Optimization Over Multiple Discrete Variables
Taehyeon Kim, Jaeyeon Ahn, Nakyil Kim +1
In the machine learning algorithms, the choice of the hyperparameter is often an art more than a science, requiring labor-intensive search with expert experience. Therefore, automa…
Accurate and Fast Federated Learning via Combinatorial Multi-Armed Bandits
Taehyeon Kim, Sangmin Bae, Jin-woo Lee +1
Federated learning has emerged as an innovative paradigm of collaborative machine learning. Unlike conventional machine learning, a global model is collaboratively learned while da…