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stat.ML2025
Towards Understanding Generalization in DP-GD: A Case Study in Training Two-Layer CNNs
Zhongjie Shi, Puyu Wang, Chenyang Zhang +1
Modern deep learning techniques focus on extracting intricate information from data to achieve accurate predictions. However, the training datasets may be crowdsourced and include…
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…