Bharath K. Sriperumbudur

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Penn State University ∙ University of California, San Diego

Mean Shrinkage Estimation for High-Dimensional Diagonal Natural Exponential Families

Shrinkage estimators have been studied widely in statistics and have pro...
10/15/2020 ∙ by Nikolas Siapoutis, et al. ∙ 0 ∙ share

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On Distance and Kernel Measures of Conditional Independence

Measuring conditional independence is one of the important tasks in stat...
12/02/2019 ∙ by Tianhong Sheng, et al. ∙ 0 ∙ share

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Kernel Sketching yields Kernel JL

The main contribution of the paper is to show that Gaussian sketching of...
08/16/2019 ∙ by Samory Kpotufe, et al. ∙ 0 ∙ share

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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...
02/01/2019 ∙ by Joseph Lam-Weil, et al. ∙ 0 ∙ share

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On Kernel Derivative Approximation with Random Fourier Features

Random Fourier features (RFF) represent one of the most popular and wide...
10/11/2018 ∙ by Zoltan Szabo, et al. ∙ 0 ∙ share

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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...
07/06/2018 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ share

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Minimax Estimation of Quadratic Fourier Functionals

We study estimation of (semi-)inner products between two nonparametric p...
03/30/2018 ∙ by Shashank Singh, et al. ∙ 0 ∙ share

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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...
09/01/2017 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ share

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Characteristic and Universal Tensor Product Kernels

Kernel mean embeddings provide a versatile and powerful nonparametric re...
08/28/2017 ∙ by Zoltan Szabo, et al. ∙ 0 ∙ share

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Adaptive Clustering Using Kernel Density Estimators

We investigate statistical properties of a clustering algorithm that rec...
08/17/2017 ∙ by Ingo Steinwart, et al. ∙ 0 ∙ share

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Convergence guarantees for kernel-based quadrature rules in misspecified settings

Kernel-based quadrature rules are becoming important in machine learning...
05/24/2016 ∙ by Motonobu Kanagawa, et al. ∙ 0 ∙ share

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Optimal Rates for Random Fourier Features

Kernel methods represent one of the most powerful tools in machine learn...
06/06/2015 ∙ by Bharath K. Sriperumbudur, et al. ∙ 0 ∙ share

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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...
05/11/2013 ∙ by Purushottam Kar, et al. ∙ 0 ∙ share

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Metric Embedding for Nearest Neighbor Classification

The distance metric plays an important role in nearest neighbor (NN) cla...
06/24/2007 ∙ by Bharath K. Sriperumbudur, et al. ∙ 0 ∙ share

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Bharath K. Sriperumbudur

Featured Co-authors

Barnabas Poczos

Prateek Jain

Philipp Hennig

Kenji Fukumizu

Dino Sejdinovic

Alexandra Carpentier

Purushottam Kar

Ingo Steinwart

Zoltan Szabo

Shashank Singh

Samory Kpotufe

Mean Shrinkage Estimation for High-Dimensional Diagonal Natural Exponential Families

On Distance and Kernel Measures of Conditional Independence

Kernel Sketching yields Kernel JL

Local minimax rates for closeness testing of discrete distributions

On Kernel Derivative Approximation with Random Fourier Features

Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences

Minimax Estimation of Quadratic Fourier Functionals

Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings

Characteristic and Universal Tensor Product Kernels

Adaptive Clustering Using Kernel Density Estimators

Convergence guarantees for kernel-based quadrature rules in misspecified settings

Optimal Rates for Random Fourier Features

On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions

Metric Embedding for Nearest Neighbor Classification