Fanghui Liu

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Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

In this paper, we develop a quadrature framework for large-scale kernel ...
11/03/2020 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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Kernel regression in high dimension: Refined analysis beyond double descent

In this paper, we provide a precise characterize of generalization prope...
10/06/2020 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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

Random Fourier features enable researchers to build feature map to learn...
09/10/2020 ∙ by Kun Fang, et al. ∙ 0 ∙ share

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Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces

In this paper, we study the asymptotical properties of least squares reg...
06/01/2020 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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Generalizing Random Fourier Features via Generalized Measures

We generalize random Fourier features, that usually require kernel funct...
05/30/2020 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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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...
04/23/2020 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

In this paper, we propose a fast surrogate leverage weighted sampling st...
11/20/2019 ∙ by Fanghui Liu, et al. ∙ 16 ∙ share

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Deep Kernel Learning via Random Fourier Features

Kernel learning methods are among the most effective learning methods an...
10/07/2019 ∙ by Jiaxuan Xie, et al. ∙ 0 ∙ share

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Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

In this paper, we propose a novel matching based tracker by investigatin...
04/15/2019 ∙ by Fanghui Liu, et al. ∙ 26 ∙ share

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

Hyper-kernels endowed by hyper-Reproducing Kernel Hilbert Space (hyper-R...
09/26/2018 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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Learning Data-adaptive Nonparametric Kernels

Traditional kernels or their combinations are often not sufficiently fle...
08/31/2018 ∙ by Fanghui Liu, et al. ∙ 8 ∙ share

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Indefinite Kernel Logistic Regression

Traditionally, kernel learning methods requires positive definitiveness ...
07/06/2017 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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Visual Tracking via Nonnegative Regularization Multiple Locality Coding

This paper presents a novel object tracking method based on approximated...
10/05/2015 ∙ by Fanghui Liu, et al. ∙ 0 ∙ share

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Fanghui Liu

Featured Co-authors

Dacheng Tao

Jie Yang

Li Li

Yudong Chen

Xiaolin Huang

Johan A. K. Suykens

Lei Shi

Tao Zhou

Chen gong

Zhenyu Liao

Kun Fang

Towards a Unified Quadrature Framework for Large-Scale Kernel Machines

Kernel regression in high dimension: Refined analysis beyond double descent

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

Analysis of Least Squares Regularized Regression in Reproducing Kernel Krein Spaces

Generalizing Random Fourier Features via Generalized Measures

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

Random Fourier Features via Fast Surrogate Leverage Weighted Sampling

Deep Kernel Learning via Random Fourier Features

Robust Visual Tracking Revisited: From Correlation Filter to Template Matching

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

Learning Data-adaptive Nonparametric Kernels

Indefinite Kernel Logistic Regression

Visual Tracking via Nonnegative Regularization Multiple Locality Coding