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07/14/2023
Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood
Gaussian process (GP) regression is a Bayesian nonparametric method for ...
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03/07/2023
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
This paper investigates when the importance weighting (IW) correction is...
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01/21/2022
Improved Random Features for Dot Product Kernels
Dot product kernels, such as polynomial and exponential (softmax) kernel...
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06/02/2021
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
We investigate the connections between sparse approximation methods for ...
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05/24/2019
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical i...
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02/07/2019
Model Selection for Simulator-based Statistical Models: A Kernel Approach
We propose a novel approach to model selection for simulator-based stati...
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12/20/2018
On the positivity and magnitudes of Bayesian quadrature weights
This article reviews and studies the properties of Bayesian quadrature w...
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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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05/22/2018
Counterfactual Mean Embedding: A Kernel Method for Nonparametric Causal Inference
This paper introduces a novel Hilbert space representation of a counterf...
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02/23/2018
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
We propose a novel approach to parameter estimation for simulator-based ...
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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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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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12/17/2013
