1 citations · 2 across the 6 of their papers we have counts for
6 papers
Preference Data Selection for Mitigating the Alignment Tax in Large Language Models
Minsu Kim, Jianxun Lian, Xing Xie +1
Aligning large language models to human preferences is crucial for real-world deployment but frequently incurs an alignment tax, leading to the catastrophic forgetting of pre-train…
GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
In the context of continual learning, acquiring new knowledge while maintaining previous knowledge presents a significant challenge. Existing methods often use experience replay te…
T-CIL: Temperature Scaling using Adversarial Perturbation for Calibration in Class-Incremental Learning
Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
We study model confidence calibration in class-incremental learning, where models learn from sequential tasks with different class sets. While existing works primarily focus on acc…
Fair Class-Incremental Learning using Sample Weighting
Jaeyoung Park, Minsu Kim, Steven Euijong Whang
Model fairness is becoming important in class-incremental learning for Trustworthy AI. While accuracy has been a central focus in class-incremental learning, fairness has been rela…
RC-Mixup: A Data Augmentation Strategy against Noisy Data for Regression Tasks
Seong-Hyeon Hwang, Minsu Kim, Steven Euijong Whang
We study the problem of robust data augmentation for regression tasks in the presence of noisy data. Data augmentation is essential for generalizing deep learning models, but most…
Quilt: Robust Data Segment Selection against Concept Drifts
Minsu Kim, Seong-Hyeon Hwang, Steven Euijong Whang
Continuous machine learning pipelines are common in industrial settings where models are periodically trained on data streams. Unfortunately, concept drifts may occur in data strea…