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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 ...
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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...
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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...
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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...
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Generalizing Random Fourier Features via Generalized Measures
We generalize random Fourier features, that usually require kernel funct...
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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...
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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...
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Deep Kernel Learning via Random Fourier Features
Kernel learning methods are among the most effective learning methods an...
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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...
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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...
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Learning Data-adaptive Nonparametric Kernels
Traditional kernels or their combinations are often not sufficiently fle...
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Indefinite Kernel Logistic Regression
Traditionally, kernel learning methods requires positive definitiveness ...
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Visual Tracking via Nonnegative Regularization Multiple Locality Coding
This paper presents a novel object tracking method based on approximated...
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