Dino Sejdinovic

research

∙ 07/14/2023

FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation

Emulators, or reduced complexity climate models, are surrogate Earth sys...

0 Shahine Bouabid, et al. ∙

research

∙ 05/24/2023

Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models

We present a novel approach for explaining Gaussian processes (GPs) that...

0 Siu Lun Chau, et al. ∙

research

∙ 05/24/2023

A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods

We establish the first mathematically rigorous link between Bayesian, va...

0 Veit David Wild, et al. ∙

research

∙ 05/22/2023

Squared Neural Families: A New Class of Tractable Density Models

Flexible models for probability distributions are an essential ingredien...

0 Russell Tsuchida, et al. ∙

research

∙ 01/26/2023

Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge

A directed acyclic graph (DAG) provides valuable prior knowledge that is...

0 Shahine Bouabid, et al. ∙

research

∙ 12/09/2022

Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects Even Under One Sided Overlap

As causal inference becomes more widespread the importance of having goo...

0 Jake Fawkes, et al. ∙

research

∙ 11/02/2022

Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation

The counterfactual distribution models the effect of the treatment in th...

0 Diego Martinez-Taboada, et al. ∙

research

∙ 10/25/2022

Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings

The problem of sequentially maximizing the expectation of a function see...

0 Diego Martinez-Taboada, et al. ∙

research

∙ 08/07/2022

Kernel Biclustering algorithm in Hilbert Spaces

Biclustering algorithms partition data and covariates simultaneously, pr...

12 Marcos Matabuena, et al. ∙

research

∙ 05/26/2022

Explaining Preferences with Shapley Values

While preference modelling is becoming one of the pillars of machine lea...

0 Robert Hu, et al. ∙

research

∙ 05/12/2022

Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning

We develop a framework for generalized variational inference in infinite...

0 Veit D. Wild, et al. ∙

research

∙ 05/06/2022

AODisaggregation: toward global aerosol vertical profiles

Aerosol-cloud interactions constitute the largest source of uncertainty ...

3 Shahine Bouabid, et al. ∙

research

∙ 02/28/2022

Selection, Ignorability and Challenges With Causal Fairness

In this paper we look at popular fairness methods that use causal counte...

0 Jake Fawkes, et al. ∙

research

∙ 02/02/2022

Giga-scale Kernel Matrix Vector Multiplication on GPU

Kernel matrix-vector multiplication (KMVM) is a foundational operation i...

0 Robert Hu, et al. ∙

research

∙ 02/01/2022

Identifying Pauli spin blockade using deep learning

Pauli spin blockade (PSB) can be employed as a great resource for spin q...

0 Jonas Schuff, et al. ∙

research

∙ 11/25/2021

A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment

Causal inference grows increasingly complex as the number of confounders...

0 Robert Hu, et al. ∙

research

∙ 10/18/2021

RKHS-SHAP: Shapley Values for Kernel Methods

Feature attribution for kernel methods is often heuristic and not indivi...

0 Siu Lun Chau, et al. ∙

research

∙ 06/10/2021

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 ...

0 Valerie C. Bradley, et al. ∙

research

∙ 06/07/2021

BayesIMP: Uncertainty Quantification for Causal Data Fusion

While causal models are becoming one of the mainstays of machine learnin...

0 Siu Lun Chau, et al. ∙

research

∙ 06/02/2021

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

We investigate the connections between sparse approximation methods for ...

0 Veit Wild, et al. ∙

research

∙ 05/27/2021

Deconditional Downscaling with Gaussian Processes

Refining low-resolution (LR) spatial fields with high-resolution (HR) in...

0 Siu Lun Chau, et al. ∙

research

∙ 05/04/2021

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...

0 Harrison Zhu, et al. ∙

research

∙ 03/26/2021

Time-to-event regression using partially monotonic neural networks

We propose a novel method, termed SuMo-net, that uses partially monotoni...

0 David Rindt, et al. ∙

research

∙ 11/01/2020

Inter-domain Deep Gaussian Processes

Inter-domain Gaussian processes (GPs) allow for high flexibility and low...

0 Tim G. J. Rudner, et al. ∙

research

∙ 08/23/2020

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

The problem of graph learning concerns the construction of an explicit t...

40 Xingyue Pu, et al. ∙

research

∙ 08/06/2020

Benign Overfitting and Noisy Features

Modern machine learning often operates in the regime where the number of...

21 Zhu Li, et al. ∙

research

∙ 07/10/2020

Variational Inference with Continuously-Indexed Normalizing Flows

Continuously-indexed flows (CIFs) have recently achieved improvements ov...

11 Anthony Caterini, et al. ∙

research

∙ 07/02/2020

A Perspective on Gaussian Processes for Earth Observation

Earth observation (EO) by airborne and satellite remote sensing and in-s...

0 Gustau Camps-Valls, et al. ∙

research

∙ 06/06/2020

Learning Inconsistent Preferences with Kernel Methods

We propose a probabilistic kernel approach for preferential learning fro...

0 Siu Lun Chau, et al. ∙

research

∙ 05/13/2020

Consistency of permutation tests for HSIC and dHSIC

The Hilbert–Schmidt Independence Criterion (HSIC) is a popular measure o...

0 David Rindt, et al. ∙

research

∙ 05/08/2020

Spectral Ranking with Covariates

We consider approaches to the classical problem of establishing a statis...

5 Siu Lun Chau, et al. ∙

research

∙ 02/13/2020

Bayesian Kernel Two-Sample Testing

In modern data analysis, nonparametric measures of discrepancies between...

0 Qinyi Zhang, et al. ∙

research

∙ 02/11/2020

Large Scale Tensor Regression using Kernels and Variational Inference

We outline an inherent weakness of tensor factorization models when late...

24 Robert Hu, et al. ∙

research

∙ 01/10/2020

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

The discussion focuses on metric covariance, a new association measure b...

0 Dino Sejdinovic, et al. ∙

research

∙ 12/08/2019

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

With the incorporation of new data gathering methods in clinical researc...

9 Tamara Fernández, et al. ∙

research

∙ 11/29/2019

Detecting anthropogenic cloud perturbations with deep learning

One of the most pressing questions in climate science is that of the eff...

2 Duncan Watson-Parris, et al. ∙

research

∙ 11/11/2019

Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness

Current adoption of machine learning in industrial, societal and economi...

20 Zhu Li, et al. ∙

research

∙ 06/10/2019

Nonparametric Independence Testing for Right-Censored Data using Optimal Transport

We propose a nonparametric test of independence, termed OPT-HSIC, betwee...

3 David Rindt, et al. ∙

research

∙ 06/05/2019

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

Current meta-learning approaches focus on learning functional representa...

6 Jean-Francois Ton, et al. ∙

research

∙ 11/26/2018

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

This article is the rejoinder for the paper "Probabilistic Integration: ...

18 Francois-Xavier Briol, et al. ∙

research

∙ 10/15/2018

Hyperparameter Learning via Distributional Transfer

Bayesian optimisation is a popular technique for hyperparameter learning...

0 Ho Chung Leon Law, et al. ∙

research

∙ 08/01/2018

A Differentially Private Kernel Two-Sample Test

Kernel two-sample testing is a useful statistical tool in determining wh...

2 Anant Raj, et al. ∙

research

∙ 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...

0 Motonobu Kanagawa, et al. ∙

research

∙ 06/24/2018

A Unified Analysis of Random Fourier Features

We provide the first unified theoretical analysis of supervised learning...

0 Zhu Li, et al. ∙

research

∙ 05/29/2018

Hamiltonian Variational Auto-Encoder

Variational Auto-Encoders (VAEs) have become very popular techniques to ...

0 Anthony L. Caterini, et al. ∙

research

∙ 05/22/2018

Variational Learning on Aggregate Outputs with Gaussian Processes

While a typical supervised learning framework assumes that the inputs an...

0 Ho Chung Leon Law, et al. ∙

research

∙ 04/12/2018

Causal Inference via Kernel Deviance Measures

Discovering the causal structure among a set of variables is a fundament...

0 Jovana Mitrovic, et al. ∙

research

∙ 11/15/2017

Spatial Mapping with Gaussian Processes and Nonstationary Fourier Features

The use of covariance kernels is ubiquitous in the field of spatial stat...

0 Jean-Francois Ton, et al. ∙

research

∙ 05/11/2017

Bayesian Approaches to Distribution Regression

Distribution regression has recently attracted much interest as a generi...

0 Ho Chung Leon Law, et al. ∙

research

∙ 03/22/2017

Testing and Learning on Distributions with Symmetric Noise Invariance

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD...

0 Ho Chung Leon Law, et al. ∙

Dino Sejdinovic

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