Dino Sejdinovic

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University of Oxford

Associate Professor at University of Oxford

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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Dino Sejdinovic

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