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Mean Shrinkage Estimation for High-Dimensional Diagonal Natural Exponential Families
Shrinkage estimators have been studied widely in statistics and have pro...
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On Distance and Kernel Measures of Conditional Independence
Measuring conditional independence is one of the important tasks in stat...
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Kernel Sketching yields Kernel JL
The main contribution of the paper is to show that Gaussian sketching of...
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Local minimax rates for closeness testing of discrete distributions
We consider the closeness testing (or two-sample testing) problem in the...
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On Kernel Derivative Approximation with Random Fourier Features
Random Fourier features (RFF) represent one of the most popular and wide...
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Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
This paper is an attempt to bridge the conceptual gaps between researche...
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Minimax Estimation of Quadratic Fourier Functionals
We study estimation of (semi-)inner products between two nonparametric p...
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Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
This paper presents convergence analysis of kernel-based quadrature rule...
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Characteristic and Universal Tensor Product Kernels
Kernel mean embeddings provide a versatile and powerful nonparametric re...
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Adaptive Clustering Using Kernel Density Estimators
We investigate statistical properties of a clustering algorithm that rec...
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Convergence guarantees for kernel-based quadrature rules in misspecified settings
Kernel-based quadrature rules are becoming important in machine learning...
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Optimal Rates for Random Fourier Features
Kernel methods represent one of the most powerful tools in machine learn...
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On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions
In this paper, we study the generalization properties of online learning...
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Metric Embedding for Nearest Neighbor Classification
The distance metric plays an important role in nearest neighbor (NN) cla...
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