31 citations · 71 across the 9 of their papers we have counts for
4 papers · 1 filter
Retrieval-Augmented Generation as Noisy In-Context Learning: A Unified Theory and Risk Bounds
Yang Guo, Yutian Tao, Yifei Ming +2
Retrieval-augmented generation (RAG) has seen many empirical successes in recent years by aiding the LLM with external knowledge. However, its theoretical aspect has remained mostl…
Understanding Retrieval-Augmented Task Adaptation for Vision-Language Models
Yifei Ming, Yixuan Li
Pre-trained contrastive vision-language models have demonstrated remarkable performance across a wide range of tasks. However, they often struggle on fine-trained datasets with cat…
HYPO: Hyperspherical Out-of-Distribution Generalization
Haoyue Bai, Yifei Ming, Julian Katz-Samuels +1
Out-of-distribution (OOD) generalization is critical for machine learning models deployed in the real world. However, achieving this can be fundamentally challenging, as it require…
Domain Generalization via Nuclear Norm Regularization
Zhenmei Shi, Yifei Ming, Ying Fan +2
The ability to generalize to unseen domains is crucial for machine learning systems deployed in the real world, especially when we only have data from limited training domains. In…