
Interdomain Deep Gaussian Processes
Interdomain Gaussian processes (GPs) allow for high flexibility and low...
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Kernelbased Graph Learning from Smooth Signals: A Functional Viewpoint
The problem of graph learning concerns the construction of an explicit t...
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Benign Overfitting and Noisy Features
Modern machine learning often operates in the regime where the number of...
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Variational Inference with ContinuouslyIndexed Normalizing Flows
Continuouslyindexed flows (CIFs) have recently achieved improvements ov...
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A Perspective on Gaussian Processes for Earth Observation
Earth observation (EO) by airborne and satellite remote sensing and ins...
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Learning Inconsistent Preferences with Kernel Methods
We propose a probabilistic kernel approach for preferential learning fro...
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Consistency of permutation tests for HSIC and dHSIC
The Hilbert–Schmidt Independence Criterion (HSIC) is a popular measure o...
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Spectral Ranking with Covariates
We consider approaches to the classical problem of establishing a statis...
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Bayesian Kernel TwoSample Testing
In modern data analysis, nonparametric measures of discrepancies between...
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Large Scale Tensor Regression using Kernels and Variational Inference
We outline an inherent weakness of tensor factorization models when late...
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Discussion of "Functional Models for TimeVarying Random Objects” by Dubey and Müller
The discussion focuses on metric covariance, a new association measure b...
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A kernel logrank test of independence for rightcensored data
With the incorporation of new data gathering methods in clinical researc...
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Detecting anthropogenic cloud perturbations with deep learning
One of the most pressing questions in climate science is that of the eff...
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Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Current adoption of machine learning in industrial, societal and economi...
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Nonparametric Independence Testing for RightCensored Data using Optimal Transport
We propose a nonparametric test of independence, termed OPTHSIC, betwee...
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Noise Contrastive MetaLearning for Conditional Density Estimation using Kernel Mean Embeddings
Current metalearning approaches focus on learning functional representa...
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Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
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Hyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning...
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A Differentially Private Kernel TwoSample Test
Kernel twosample testing is a useful statistical tool in determining wh...
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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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A Unified Analysis of Random Fourier Features
We provide the first unified theoretical analysis of supervised learning...
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Hamiltonian Variational AutoEncoder
Variational AutoEncoders (VAEs) have become very popular techniques to ...
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Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs an...
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Causal Inference via Kernel Deviance Measures
Discovering the causal structure among a set of variables is a fundament...
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Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features
The use of covariance kernels is ubiquitous in the field of spatial stat...
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Bayesian Approaches to Distribution Regression
Distribution regression has recently attracted much interest as a generi...
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Testing and Learning on Distributions with Symmetric Noise Invariance
Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD...
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Poisson intensity estimation with reproducing kernels
Despite the fundamental nature of the inhomogeneous Poisson process in t...
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LargeScale Kernel Methods for Independence Testing
Representations of probability measures in reproducing kernel Hilbert sp...
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Hyperspectral Image Classification with Support Vector Machines on Kernel Distribution Embeddings
We propose a novel approach for pixel classification in hyperspectral im...
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Bayesian Learning of Kernel Embeddings
Kernel methods are one of the mainstays of machine learning, but the pro...
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DRABC: Approximate Bayesian Computation with KernelBased Distribution Regression
Performing exact posterior inference in complex generative models is oft...
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Probabilistic Integration: A Role in Statistical Computation?
A research frontier has emerged in scientific computation, wherein numer...
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Kernel Sequential Monte Carlo
We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
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Fast TwoSample Testing with Analytic Representations of Probability Measures
We propose a class of nonparametric twosample tests with a cost linear ...
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Gradientfree Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradientfree adaptiv...
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KernelBased JustInTime Learning for Passing Expectation Propagation Messages
We propose an efficient nonparametric strategy for learning a message op...
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K2ABC: Approximate Bayesian Computation with Kernel Embeddings
Complicated generative models often result in a situation where computin...
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Unbiased Bayes for Big Data: Paths of Partial Posteriors
A key quantity of interest in Bayesian inference are expectations of fun...
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Kernel Adaptive MetropolisHastings
A Kernel Adaptive MetropolisHastings algorithm is introduced, for the p...
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A Kernel Test for ThreeVariable Interactions
We introduce kernel nonparametric tests for Lancaster threevariable int...
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Equivalence of distancebased and RKHSbased statistics in hypothesis testing
We provide a unifying framework linking two classes of statistics used i...
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Hypothesis testing using pairwise distances and associated kernels (with Appendix)
We provide a unifying framework linking two classes of statistics used i...
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Dino Sejdinovic
verfied profile
Associate Professor at University of Oxford