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06/29/2023
Kernel ε-Greedy for Contextual Bandits
We consider a kernelized version of the ϵ-greedy strategy for contextual...
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12/25/2022
Gromov-Wasserstein Distances: Entropic Regularization, Duality, and Sample Complexity
The Gromov-Wasserstein (GW) distance quantifies dissimilarity between me...
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11/15/2022
Regularized Stein Variational Gradient Flow
The Stein Variational Gradient Descent (SVGD) algorithm is an determinis...
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07/13/2022
Shrinkage Estimation of Higher Order Bochner Integrals
We consider shrinkage estimation of higher order Hilbert space valued Bo...
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06/08/2022
Unified RKHS Methodology and Analysis for Functional Linear and Single-Index Models
Functional linear and single-index models are core regression methods in...
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11/22/2021
Cycle Consistent Probability Divergences Across Different Spaces
Discrepancy measures between probability distributions are at the core o...
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10/15/2020
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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12/02/2019
On Distance and Kernel Measures of Conditional Independence
Measuring conditional independence is one of the important tasks in stat...
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08/16/2019
Kernel Sketching yields Kernel JL
The main contribution of the paper is to show that Gaussian sketching of...
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02/01/2019
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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10/11/2018
On Kernel Derivative Approximation with Random Fourier Features
Random Fourier features (RFF) represent one of the most popular and wide...
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07/06/2018
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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03/30/2018
Minimax Estimation of Quadratic Fourier Functionals
We study estimation of (semi-)inner products between two nonparametric p...
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09/01/2017
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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08/28/2017
Characteristic and Universal Tensor Product Kernels
Kernel mean embeddings provide a versatile and powerful nonparametric re...
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08/17/2017
Adaptive Clustering Using Kernel Density Estimators
We investigate statistical properties of a clustering algorithm that rec...
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05/24/2016
Convergence guarantees for kernel-based quadrature rules in misspecified settings
Kernel-based quadrature rules are becoming important in machine learning...
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06/06/2015
Optimal Rates for Random Fourier Features
Kernel methods represent one of the most powerful tools in machine learn...
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05/11/2013
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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06/24/2007