15 citations · 21 across the 11 of their papers we have counts for
6 papers · 1 filter
PLC-DPO: Posterior Label Correction in Noisy and Ambiguous Preference Optimization
Boryeong Cho, Sumyeong Ahn, Se-Young Yun
Direct Preference Optimization (DPO) simplifies alignment through pairwise comparisons but assumes all observed preferences are reliable. Real data often violates this assumption,…
FOAM: Frequency and Operator Error-Based Adaptive Damping Method for Reducing Staleness-Oriented Error for Shampoo
Kyunghun Nam, Sumyeong Ahn
Shampoo is attracting considerable attention for its superior performance on large-scale optimization benchmarks; yet it faces a significant practical bottleneck: the prohibitive c…
Fine tuning Pre trained Models for Robustness Under Noisy Labels
Sumyeong Ahn, Sihyeon Kim, Jongwoo Ko +1
The presence of noisy labels in a training dataset can significantly impact the performance of machine learning models. To tackle this issue, researchers have explored methods for…
Denoising after Entropy-based Debiasing A Robust Training Method for Dataset Bias with Noisy Labels
Sumyeong Ahn, Se-Young Yun
Improperly constructed datasets can result in inaccurate inferences. For instance, models trained on biased datasets perform poorly in terms of generalization (i.e., dataset bias).…
Mitigating Dataset Bias by Using Per-sample Gradient
Sumyeong Ahn, Seongyoon Kim, Se-young Yun
The performance of deep neural networks is strongly influenced by the training dataset setup. In particular, when attributes having a strong correlation with the target attribute a…
Enlarging Discriminative Power by Adding an Extra Class in Unsupervised Domain Adaptation
Hai H. Tran, Sumyeong Ahn, Taeyoung Lee +1
In this paper, we study the problem of unsupervised domain adaptation that aims at obtaining a prediction model for the target domain using labeled data from the source domain and…