182 citations · 366 across the 22 of their papers we have counts for
25 papers
Federated Learning under Covariate Shifts with Generalization Guarantees
Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick +2
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance. To handle covariate shifts, w…
Benign Overfitting in Deep Neural Networks under Lazy Training
Zhenyu Zhu, Fanghui Liu, Grigorios G Chrysos +2
This paper focuses on over-parameterized deep neural networks (DNNs) with ReLU activation functions and proves that when the data distribution is well-separated, DNNs can achieve B…
Regularization of polynomial networks for image recognition
Grigorios G Chrysos, Bohan Wang, Jiankang Deng +1
Deep Neural Networks (DNNs) have obtained impressive performance across tasks, however they still remain as black boxes, e.g., hard to theoretically analyze. At the same time, Poly…
Revisiting adversarial training for the worst-performing class
Thomas Pethick, Grigorios G. Chrysos, Volkan Cevher
Despite progress in adversarial training (AT), there is a substantial gap between the top-performing and worst-performing classes in many datasets. For example, on CIFAR10, the acc…
Sound and Complete Verification of Polynomial Networks
Elias Abad Rocamora, Mehmet Fatih Sahin, Fanghui Liu +2
Polynomial Networks (PNs) have demonstrated promising performance on face and image recognition recently. However, robustness of PNs is unclear and thus obtaining certificates beco…
Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study
Yongtao Wu, Zhenyu Zhu, Fanghui Liu +2
Neural tangent kernel (NTK) is a powerful tool to analyze training dynamics of neural networks and their generalization bounds. The study on NTK has been devoted to typical neural…