67 citations · 176 across the 24 of their papers we have counts for
6 papers · 1 filter
Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
Zhongjie Shi, Fanghui Liu, Yuan Cao +1
Adversarial training is a widely used method to improve the robustness of deep neural networks (DNNs) over adversarial perturbations. However, it is empirically observed that adver…
What can online reinforcement learning with function approximation benefit from general coverage conditions?
Fanghui Liu, Luca Viano, Volkan Cevher
In online reinforcement learning (RL), instead of employing standard structural assumptions on Markov decision processes (MDPs), using a certain coverage condition (original from o…
On the Double Descent of Random Features Models Trained with SGD
Fanghui Liu, Johan A. K. Suykens, Volkan Cevher
We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/over-parameterized regime. In this…
Kernel regression in high dimensions: Refined analysis beyond double descent
Fanghui Liu, Zhenyu Liao, Johan A. K. Suykens
In this paper, we provide a precise characterization of generalization properties of high dimensional kernel ridge regression across the under- and over-parameterized regimes, depe…
Analysis of Regularized Least Squares in Reproducing Kernel Krein Spaces
Fanghui Liu, Lei Shi, Xiaolin Huang +2
In this paper, we study the asymptotic properties of regularized least squares with indefinite kernels in reproducing kernel Krein spaces (RKKS). By introducing a bounded hyper-sph…
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond
Fanghui Liu, Xiaolin Huang, Yudong Chen +1
Random features is one of the most popular techniques to speed up kernel methods in large-scale problems. Related works have been recognized by the NeurIPS Test-of-Time award in 20…