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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

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Kernel Regression for Graph Signal Prediction in Presence of Sparse Noise

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Kernel Density Estimation through Density Constrained Near Neighbor Search

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Oblivious Sketching of High-Degree Polynomial Kernels

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

From averaging to acceleration, there is only a step-size

Kernel Density Estimation through Density Constrained Near Neighbor Search

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Neural Tanget Kernel: Convergence and Generalization in Neural Networks

Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms

Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression

A Probabilistic Kernel Method for Human Mobility Prediction with Smartphones

Particle number control for direct simulation Monte-Carlo methodology using kernel estimates

Oblivious Sketching of High-Degree Polynomial Kernels

From averaging to acceleration, there is only a step-size