Fanghui Liu | DeepAI

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∙ 05/30/2023

Benign Overfitting in Deep Neural Networks under Lazy Training

This paper focuses on over-parameterized deep neural networks (DNNs) wit...

0 Zhenyu Zhu, et al. ∙

∙ 04/25/2023

Provable benefits of general coverage conditions in efficient online RL with function approximation

In online reinforcement learning (RL), instead of employing standard str...

0 Fanghui Liu, et al. ∙

∙ 09/18/2022

Random Fourier Features for Asymmetric Kernels

The random Fourier features (RFFs) method is a powerful and popular tech...

0 Mingzhen He, et al. ∙

∙ 09/16/2022

Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study

Neural tangent kernel (NTK) is a powerful tool to analyze training dynam...

0 Yongtao Wu, et al. ∙

∙ 09/15/2022

Understanding Deep Neural Function Approximation in Reinforcement Learning via ε-Greedy Exploration

This paper provides a theoretical study of deep neural function approxim...

0 Fanghui Liu, et al. ∙

∙ 09/15/2022

Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization)

We study the average robustness notion in deep neural networks in (selec...

0 Zhenyu Zhu, et al. ∙

∙ 09/15/2022

Generalization Properties of NAS under Activation and Skip Connection Search

Neural Architecture Search (NAS) has fostered the automatic discovery of...

0 Zhenyu Zhu, et al. ∙

∙ 09/15/2022

Sound and Complete Verification of Polynomial Networks

Polynomial Networks (PNs) have demonstrated promising performance on fac...

0 Elías Abad-Rocamora, et al. ∙

∙ 10/13/2021

On the Double Descent of Random Features Models Trained with SGD

We study generalization properties of random features (RF) regression in...

0 Fanghui Liu, et al. ∙

∙ 11/03/2020

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

In this paper, we develop a quadrature framework for large-scale kernel ...

0 Fanghui Liu, et al. ∙

∙ 10/06/2020

Kernel regression in high dimension: Refined analysis beyond double descent

In this paper, we provide a precise characterize of generalization prope...

0 Fanghui Liu, et al. ∙

∙ 09/10/2020

End-to-end Kernel Learning via Generative Random Fourier Features

Random Fourier features enable researchers to build feature map to learn...

0 Kun Fang, et al. ∙

∙ 06/01/2020

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces

In this paper, we study the asymptotical properties of least squares reg...

0 Fanghui Liu, et al. ∙

∙ 05/30/2020

Generalizing Random Fourier Features via Generalized Measures

We generalize random Fourier features, that usually require kernel funct...

0 Fanghui Liu, et al. ∙

∙ 04/23/2020

Random Features for Kernel Approximation: A Survey in Algorithms, Theory, and Beyond

Random features is one of the most sought-after research topics in stati...

0 Fanghui Liu, et al. ∙

∙ 11/20/2019

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling st...

16 Fanghui Liu, et al. ∙

∙ 10/07/2019

Deep Kernel Learning via Random Fourier Features

Kernel learning methods are among the most effective learning methods an...

0 Jiaxuan Xie, et al. ∙

∙ 04/15/2019

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

In this paper, we propose a novel matching based tracker by investigatin...

26 Fanghui Liu, et al. ∙

∙ 09/26/2018

Generalization Properties of hyper-RKHS and its Application to Out-of-Sample Extensions

Hyper-kernels endowed by hyper-Reproducing Kernel Hilbert Space (hyper-R...

0 Fanghui Liu, et al. ∙

∙ 08/31/2018

Learning Data-adaptive Nonparametric Kernels

Traditional kernels or their combinations are often not sufficiently fle...

8 Fanghui Liu, et al. ∙

∙ 07/06/2017

Indefinite Kernel Logistic Regression

Traditionally, kernel learning methods requires positive definitiveness ...

0 Fanghui Liu, et al. ∙

∙ 10/05/2015

Visual Tracking via Nonnegative Regularization Multiple Locality Coding

This paper presents a novel object tracking method based on approximated...

0 Fanghui Liu, et al. ∙