15 citations · 31 across the 18 of their papers we have counts for
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stat.ML2023
Random Smoothing Regularization in Kernel Gradient Descent Learning
Liang Ding, Tianyang Hu, Jiahang Jiang +3
Random smoothing data augmentation is a unique form of regularization that can prevent overfitting by introducing noise to the input data, encouraging the model to learn more gener…
stat.ML2021★ 4 cited
Understanding Square Loss in Training Overparametrized Neural Network Classifiers
Tianyang Hu, Jun Wang, Wenjia Wang +1
Deep learning has achieved many breakthroughs in modern classification tasks. Numerous architectures have been proposed for different data structures but when it comes to the loss…
stat.ML2020★ 15 cited
Regularization Matters: A Nonparametric Perspective on Overparametrized Neural Network
Tianyang Hu, Wenjia Wang, Cong Lin +1
Overparametrized neural networks trained by gradient descent (GD) can provably overfit any training data. However, the generalization guarantee may not hold for noisy data. From a…