3 citations · 4 across the 4 of their papers we have counts for
4 papers · 1 filter
Take the Bull by the Horns: Hard Sample-Reweighted Continual Training Improves LLM Generalization
Xuxi Chen, Zhendong Wang, Daouda Sow +5
In the rapidly advancing arena of large language models (LLMs), a key challenge is to enhance their capabilities amid a looming shortage of high-quality training data. Our study st…
A Large-Scale Empirical Study on Improving the Fairness of Image Classification Models
Junjie Yang, Jiajun Jiang, Zeyu Sun +1
Fairness has been a critical issue that affects the adoption of deep learning models in real practice. To improve model fairness, many existing methods have been proposed and evalu…
Learning to Generalize Provably in Learning to Optimize
Junjie Yang, Tianlong Chen, Mingkang Zhu +4
Learning to optimize (L2O) has gained increasing popularity, which automates the design of optimizers by data-driven approaches. However, current L2O methods often suffer from poor…
M-L2O: Towards Generalizable Learning-to-Optimize by Test-Time Fast Self-Adaptation
Junjie Yang, Xuxi Chen, Tianlong Chen +2
Learning to Optimize (L2O) has drawn increasing attention as it often remarkably accelerates the optimization procedure of complex tasks by ``overfitting" specific task type, leadi…