1 citations · 1 across the 7 of their papers we have counts for
10 papers
Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence
Hyunho Choi, Jaeseung Heo, Youngbin Choi +2
Influence functions provide an efficient alternative to retraining for assessing the effects of removing or reweighting training data. For groups, however, the standard first-order…
Scaling Influence Functions in LLMs through Eigenbasis-Corrected One-Bit Gradient Projection
Jaeseung Heo, J Rosser, Dongwoo Kim
Influence functions estimate how individual training examples affect the behavior of large language models (LLMs). Analyzing how training data influence different behaviors of an L…
Interaction-Aware Influence Functions for Group Attribution
Jaeseung Heo, Kyeongheung Yun, Youngbin Choi +3
Influence functions approximate how removing a training example changes a quantity of interest, called the target function, such as a held-out loss. To estimate the influence of a…
Long Live the Librarian! A Persistent Search Sub-Agent for Energy-Efficient Multi-Agent Software Engineering Systems
Seunghyuk Cho, Sunghyun Choi, Jaeseung Heo +4
Multi-agent systems (MAS) have substantially advanced autonomous software engineering (SWE), but their growing inference energy demands raise sustainability concerns. In this paper…
Transductive Generalization via Optimal Transport and Its Application to Graph Node Classification
MoonJeong Park, Seungbeom Lee, Kyungmin Kim +5
Many existing transductive bounds rely on classical complexity measures that are computationally intractable and often misaligned with empirical behavior. In this work, we establis…
The Oversmoothing Fallacy: A Misguided Narrative in GNN Research
MoonJeong Park, Sunghyun Choi, Jaeseung Heo +2
Oversmoothing has been recognized as a main obstacle to building deep Graph Neural Networks (GNNs), limiting the performance. This position paper argues that the influence of overs…
Influence Functions for Edge Edits in Non-Convex Graph Neural Networks
Jaeseung Heo, Kyeongheung Yun, Seokwon Yoon +3
Understanding how individual edges influence the behavior of graph neural networks (GNNs) is essential for improving their interpretability and robustness. Graph influence function…
Posterior Label Smoothing for Node Classification
Jaeseung Heo, Moonjeong Park, Dongwoo Kim
Label smoothing is a widely studied regularization technique in machine learning. However, its potential for node classification in graph-structured data, spanning homophilic to he…
Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs
MoonJeong Park, Jaeseung Heo, Dongwoo Kim
Graph Neural Network (GNN) resembles the diffusion process, leading to the over-smoothing of learned representations when stacking many layers. Hence, the reverse process of messag…
EPIC: Graph Augmentation with Edit Path Interpolation via Learnable Cost
Jaeseung Heo, Seungbeom Lee, Sungsoo Ahn +1
Data augmentation plays a critical role in improving model performance across various domains, but it becomes challenging with graph data due to their complex and irregular structu…