3 papers
stat.ML2025
Transformer Learns Optimal Variable Selection in Group-Sparse Classification
Chenyang Zhang, Xuran Meng, Yuan Cao
Transformers have demonstrated remarkable success across various applications. However, the success of transformers have not been understood in theory. In this work, we give a case…
stat.ML2025
Gradient Descent Robustly Learns the Intrinsic Dimension of Data in Training Convolutional Neural Networks
Chenyang Zhang, Peifeng Gao, Difan Zou +1
Modern neural networks are usually highly over-parameterized. Behind the wide usage of over-parameterized networks is the belief that, if the data are simple, then the trained netw…
cs.CL2025
On the Robustness of Transformers against Context Hijacking for Linear Classification
Tianle Li, Chenyang Zhang, Xingwu Chen +2
Transformer-based Large Language Models (LLMs) have demonstrated powerful in-context learning capabilities. However, their predictions can be disrupted by factually correct context…