This paper addresses intra-client and inter-client covariate shifts in
f...
This paper focuses on over-parameterized deep neural networks (DNNs) wit...
In online reinforcement learning (RL), instead of employing standard
str...
The random Fourier features (RFFs) method is a powerful and popular tech...
Neural tangent kernel (NTK) is a powerful tool to analyze training dynam...
This paper provides a theoretical study of deep neural function approxim...
We study the average robustness notion in deep neural networks in (selec...
Neural Architecture Search (NAS) has fostered the automatic discovery of...
Polynomial Networks (PNs) have demonstrated promising performance on fac...
We study generalization properties of random features (RF) regression in...
In this paper, we develop a quadrature framework for large-scale kernel
...
In this paper, we provide a precise characterize of generalization prope...
Random Fourier features enable researchers to build feature map to learn...
In this paper, we study the asymptotical properties of least squares
reg...
We generalize random Fourier features, that usually require kernel funct...
Random features is one of the most sought-after research topics in
stati...
In this paper, we propose a fast surrogate leverage weighted sampling
st...
Kernel learning methods are among the most effective learning methods an...
In this paper, we propose a novel matching based tracker by investigatin...
Hyper-kernels endowed by hyper-Reproducing Kernel Hilbert Space (hyper-R...
Traditional kernels or their combinations are often not sufficiently fle...
Traditionally, kernel learning methods requires positive definitiveness ...
This paper presents a novel object tracking method based on approximated...