3 citations · 8 across the 7 of their papers we have counts for
7 papers
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…
Initialization Matters: On the Benign Overfitting of Two-Layer ReLU CNN with Fully Trainable Layers
Shuning Shang, Xuran Meng, Yuan Cao +1
Benign overfitting refers to how over-parameterized neural networks can fit training data perfectly and generalize well to unseen data. While this has been widely investigated theo…
Estimation of Out-of-Sample Sharpe Ratio for High Dimensional Portfolio Optimization
Xuran Meng, Yuan Cao, Weichen Wang
Portfolio optimization aims at constructing a realistic portfolio with significant out-of-sample performance, which is typically measured by the out-of-sample Sharpe ratio. However…
Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data
Xuran Meng, Difan Zou, Yuan Cao
Modern deep learning models are usually highly over-parameterized so that they can overfit the training data. Surprisingly, such overfitting neural networks can usually still achie…
Per-Example Gradient Regularization Improves Learning Signals from Noisy Data
Xuran Meng, Yuan Cao, Difan Zou
Gradient regularization, as described in \citet{barrett2021implicit}, is a highly effective technique for promoting flat minima during gradient descent. Empirical evidence suggests…
Multiple Descent in the Multiple Random Feature Model
Xuran Meng, Jianfeng Yao, Yuan Cao
Recent works have demonstrated a double descent phenomenon in over-parameterized learning. Although this phenomenon has been investigated by recent works, it has not been fully und…
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping
Xuran Meng, Jianfeng Yao
Much research effort has been devoted to explaining the success of deep learning. Random Matrix Theory (RMT) provides an emerging way to this end: spectral analysis of large random…