Dino Sejdinovic

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University of Oxford

Associate Professor at University of Oxford

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Are We There Yet? Big Data Significantly Overestimates COVID-19 Vaccination in the US

Public health efforts to control the COVID-19 pandemic rely on accurate ...
06/10/2021 ∙ by Valerie C. Bradley, et al. ∙ 0 ∙ share

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BayesIMP: Uncertainty Quantification for Causal Data Fusion

While causal models are becoming one of the mainstays of machine learnin...
06/07/2021 ∙ by Siu Lun Chau, et al. ∙ 0 ∙ share

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Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes

We investigate the connections between sparse approximation methods for ...
06/02/2021 ∙ by Veit Wild, et al. ∙ 0 ∙ share

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Deconditional Downscaling with Gaussian Processes

Refining low-resolution (LR) spatial fields with high-resolution (HR) in...
05/27/2021 ∙ by Siu Lun Chau, et al. ∙ 0 ∙ share

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Multi-resolution Spatial Regression for Aggregated Data with an Application to Crop Yield Prediction

We develop a new methodology for spatial regression of aggregated output...
05/04/2021 ∙ by Harrison Zhu, et al. ∙ 0 ∙ share

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Time-to-event regression using partially monotonic neural networks

We propose a novel method, termed SuMo-net, that uses partially monotoni...
03/26/2021 ∙ by David Rindt, et al. ∙ 0 ∙ share

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Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and low...
11/01/2020 ∙ by Tim G. J. Rudner, et al. ∙ 0 ∙ share

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Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint

The problem of graph learning concerns the construction of an explicit t...
08/23/2020 ∙ by Xingyue Pu, et al. ∙ 40 ∙ share

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Benign Overfitting and Noisy Features

Modern machine learning often operates in the regime where the number of...
08/06/2020 ∙ by Zhu Li, et al. ∙ 21 ∙ share

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Variational Inference with Continuously-Indexed Normalizing Flows

Continuously-indexed flows (CIFs) have recently achieved improvements ov...
07/10/2020 ∙ by Anthony Caterini, et al. ∙ 11 ∙ share

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A Perspective on Gaussian Processes for Earth Observation

Earth observation (EO) by airborne and satellite remote sensing and in-s...
07/02/2020 ∙ by Gustau Camps-Valls, et al. ∙ 0 ∙ share

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Learning Inconsistent Preferences with Kernel Methods

We propose a probabilistic kernel approach for preferential learning fro...
06/06/2020 ∙ by Siu Lun Chau, et al. ∙ 0 ∙ share

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Consistency of permutation tests for HSIC and dHSIC

The Hilbert–Schmidt Independence Criterion (HSIC) is a popular measure o...
05/13/2020 ∙ by David Rindt, et al. ∙ 0 ∙ share

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Spectral Ranking with Covariates

We consider approaches to the classical problem of establishing a statis...
05/08/2020 ∙ by Siu Lun Chau, et al. ∙ 5 ∙ share

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Bayesian Kernel Two-Sample Testing

In modern data analysis, nonparametric measures of discrepancies between...
02/13/2020 ∙ by Qinyi Zhang, et al. ∙ 0 ∙ share

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Large Scale Tensor Regression using Kernels and Variational Inference

We outline an inherent weakness of tensor factorization models when late...
02/11/2020 ∙ by Robert Hu, et al. ∙ 24 ∙ share

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Discussion of "Functional Models for Time-Varying Random Objects” by Dubey and Müller

The discussion focuses on metric covariance, a new association measure b...
01/10/2020 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ share

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A kernel log-rank test of independence for right-censored data

With the incorporation of new data gathering methods in clinical researc...
12/08/2019 ∙ by Tamara Fernández, et al. ∙ 9 ∙ share

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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...
11/29/2019 ∙ by Duncan Watson-Parris, et al. ∙ 2 ∙ share

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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...
11/11/2019 ∙ by Zhu Li, et al. ∙ 20 ∙ share

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Nonparametric Independence Testing for Right-Censored Data using Optimal Transport

We propose a nonparametric test of independence, termed OPT-HSIC, betwee...
06/10/2019 ∙ by David Rindt, et al. ∙ 3 ∙ share

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Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings

Current meta-learning approaches focus on learning functional representa...
06/05/2019 ∙ by Jean-Francois Ton, et al. ∙ 6 ∙ share

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Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"

This article is the rejoinder for the paper "Probabilistic Integration: ...
11/26/2018 ∙ by Francois-Xavier Briol, et al. ∙ 18 ∙ share

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Hyperparameter Learning via Distributional Transfer

Bayesian optimisation is a popular technique for hyperparameter learning...
10/15/2018 ∙ by Ho Chung Leon Law, et al. ∙ 0 ∙ share

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A Differentially Private Kernel Two-Sample Test

Kernel two-sample testing is a useful statistical tool in determining wh...
08/01/2018 ∙ by Anant Raj, et al. ∙ 2 ∙ 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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A Unified Analysis of Random Fourier Features

We provide the first unified theoretical analysis of supervised learning...
06/24/2018 ∙ by Zhu Li, et al. ∙ 0 ∙ share

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Hamiltonian Variational Auto-Encoder

Variational Auto-Encoders (VAEs) have become very popular techniques to ...
05/29/2018 ∙ by Anthony L. Caterini, et al. ∙ 0 ∙ share

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Variational Learning on Aggregate Outputs with Gaussian Processes

While a typical supervised learning framework assumes that the inputs an...
05/22/2018 ∙ by Ho Chung Leon Law, et al. ∙ 0 ∙ share

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Causal Inference via Kernel Deviance Measures

Discovering the causal structure among a set of variables is a fundament...
04/12/2018 ∙ by Jovana Mitrovic, et al. ∙ 0 ∙ share

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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...
11/15/2017 ∙ by Jean-Francois Ton, et al. ∙ 0 ∙ share

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Bayesian Approaches to Distribution Regression

Distribution regression has recently attracted much interest as a generi...
05/11/2017 ∙ by Ho Chung Leon Law, et al. ∙ 0 ∙ share

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Testing and Learning on Distributions with Symmetric Noise Invariance

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD...
03/22/2017 ∙ by Ho Chung Leon Law, et al. ∙ 0 ∙ share

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Poisson intensity estimation with reproducing kernels

Despite the fundamental nature of the inhomogeneous Poisson process in t...
10/27/2016 ∙ by Seth Flaxman, et al. ∙ 0 ∙ share

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Large-Scale Kernel Methods for Independence Testing

Representations of probability measures in reproducing kernel Hilbert sp...
06/25/2016 ∙ by Qinyi Zhang, et al. ∙ 0 ∙ share

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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...
05/30/2016 ∙ by Gianni Franchi, et al. ∙ 0 ∙ share

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Bayesian Learning of Kernel Embeddings

Kernel methods are one of the mainstays of machine learning, but the pro...
03/07/2016 ∙ by Seth Flaxman, et al. ∙ 0 ∙ share

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DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Performing exact posterior inference in complex generative models is oft...
02/15/2016 ∙ by Jovana Mitrovic, et al. ∙ 0 ∙ share

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Probabilistic Integration: A Role in Statistical Computation?

A research frontier has emerged in scientific computation, wherein numer...
12/03/2015 ∙ by Francois-Xavier Briol, et al. ∙ 0 ∙ share

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Kernel Sequential Monte Carlo

We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
10/11/2015 ∙ by Ingmar Schuster, et al. ∙ 0 ∙ share

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Fast Two-Sample Testing with Analytic Representations of Probability Measures

We propose a class of nonparametric two-sample tests with a cost linear ...
06/15/2015 ∙ by Kacper Chwialkowski, et al. ∙ 0 ∙ share

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Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families

We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptiv...
06/08/2015 ∙ by Heiko Strathmann, et al. ∙ 0 ∙ share

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Kernel-Based Just-In-Time Learning for Passing Expectation Propagation Messages

We propose an efficient nonparametric strategy for learning a message op...
03/09/2015 ∙ by Wittawat Jitkrittum, et al. ∙ 0 ∙ share

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K2-ABC: Approximate Bayesian Computation with Kernel Embeddings

Complicated generative models often result in a situation where computin...
02/09/2015 ∙ by Mijung Park, et al. ∙ 0 ∙ share

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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...
01/14/2015 ∙ by Heiko Strathmann, et al. ∙ 0 ∙ share

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Kernel Adaptive Metropolis-Hastings

A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the p...
07/19/2013 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ share

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A Kernel Test for Three-Variable Interactions

We introduce kernel nonparametric tests for Lancaster three-variable int...
06/10/2013 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ share

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Equivalence of distance-based and RKHS-based statistics in hypothesis testing

We provide a unifying framework linking two classes of statistics used i...
07/25/2012 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ share

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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...
05/02/2012 ∙ by Dino Sejdinovic, et al. ∙ 0 ∙ share

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