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Found 16 papers, 2 papers with code
Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
For this optimisation, we introduce sparse regularisation and propose two methods for dealing with the issue of selecting an appropriate regularisation parameter.
When is Importance Weighting Correction Needed for Covariate Shift Adaptation?
Classic results show that the IW correction is needed when the model is parametric and misspecified.
Improved Random Features for Dot Product Kernels
These variance formulas elucidate conditions under which certain approximations (e. g., TensorSRHT) achieve lower variances than others (e. g., Rademacher sketches), and conditions under which the use of complex features leads to lower variances than real features.
Intergenerational risk sharing in a Defined Contribution pension system: analysis with Bayesian optimization
We study a fully funded, collective defined-contribution (DC) pension system with multiple overlapping generations.
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nystr\"om method and the Sparse Variational Gaussian Processes (SVGP).
Convergence Guarantees for Adaptive Bayesian Quadrature Methods
Adaptive Bayesian quadrature (ABQ) is a powerful approach to numerical integration that empirically compares favorably with Monte Carlo integration on problems of medium dimensionality (where non-adaptive quadrature is not competitive).
Model Selection for Simulator-based Statistical Models: A Kernel Approach
We propose a novel approach to model selection for simulator-based statistical models.
Simulator Calibration under Covariate Shift with Kernels
We propose a novel calibration method for computer simulators, dealing with the problem of covariate shift.
Gaussian Processes and Kernel Methods: A Review on Connections and Equivalences
This paper is an attempt to bridge the conceptual gaps between researchers working on the two widely used approaches based on positive definite kernels: Bayesian learning or inference using Gaussian processes on the one side, and frequentist kernel methods based on reproducing kernel Hilbert spaces on the other.
Counterfactual Mean Embeddings
In this work, we propose to model counterfactual distributions using a novel Hilbert space representation called counterfactual mean embedding (CME).
Kernel Recursive ABC: Point Estimation with Intractable Likelihood
We propose a novel approach to parameter estimation for simulator-based statistical models with intractable likelihood.
Convergence Analysis of Deterministic Kernel-Based Quadrature Rules in Misspecified Settings
This paper presents a convergence analysis of kernel-based quadrature rules in misspecified settings, focusing on deterministic quadrature in Sobolev spaces.
Large sample analysis of the median heuristic
In kernel methods, the median heuristic has been widely used as a way of setting the bandwidth of RBF kernels.
Convergence guarantees for kernel-based quadrature rules in misspecified settings
Kernel-based quadrature rules are becoming important in machine learning and statistics, as they achieve super-$\sqrt{n}$ convergence rates in numerical integration, and thus provide alternatives to Monte Carlo integration in challenging settings where integrands are expensive to evaluate or where integrands are high dimensional.
Model-based Kernel Sum Rule: Kernel Bayesian Inference with Probabilistic Models
Our contribution in this paper is to introduce a novel approach, termed the {\em model-based kernel sum rule} (Mb-KSR), to combine a probabilistic model and kernel Bayesian inference.
Filtering with State-Observation Examples via Kernel Monte Carlo Filter
In particular, the sampling and resampling procedures are novel in being expressed using kernel mean embeddings, so we theoretically analyze their behaviors.
