research
∙
07/14/2023
FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation
Emulators, or reduced complexity climate models, are surrogate Earth sys...
share
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...
research
∙
05/24/2023
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
We establish the first mathematically rigorous link between Bayesian, va...
research
∙
05/22/2023
Squared Neural Families: A New Class of Tractable Density Models
Flexible models for probability distributions are an essential ingredien...
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...
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...
research
∙
11/02/2022
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation
The counterfactual distribution models the effect of the treatment in th...
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...
research
∙
08/07/2022
Kernel Biclustering algorithm in Hilbert Spaces
Biclustering algorithms partition data and covariates simultaneously, pr...
research
∙
05/26/2022
Explaining Preferences with Shapley Values
While preference modelling is becoming one of the pillars of machine lea...
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...
research
∙
05/06/2022
AODisaggregation: toward global aerosol vertical profiles
Aerosol-cloud interactions constitute the largest source of uncertainty ...
research
∙
02/28/2022
Selection, Ignorability and Challenges With Causal Fairness
In this paper we look at popular fairness methods that use causal counte...
research
∙
02/02/2022
Giga-scale Kernel Matrix Vector Multiplication on GPU
Kernel matrix-vector multiplication (KMVM) is a foundational operation i...
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...
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...
research
∙
10/18/2021
RKHS-SHAP: Shapley Values for Kernel Methods
Feature attribution for kernel methods is often heuristic and not indivi...
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 ...
research
∙
06/07/2021
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learnin...
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 ...
research
∙
05/27/2021
Deconditional Downscaling with Gaussian Processes
Refining low-resolution (LR) spatial fields with high-resolution (HR) in...
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...
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...
research
∙
11/01/2020
Inter-domain Deep Gaussian Processes
Inter-domain Gaussian processes (GPs) allow for high flexibility and low...
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...
research
∙
08/06/2020
Benign Overfitting and Noisy Features
Modern machine learning often operates in the regime where the number of...
research
∙
07/10/2020
Variational Inference with Continuously-Indexed Normalizing Flows
Continuously-indexed flows (CIFs) have recently achieved improvements ov...
research
∙
07/02/2020
A Perspective on Gaussian Processes for Earth Observation
Earth observation (EO) by airborne and satellite remote sensing and in-s...
research
∙
06/06/2020
Learning Inconsistent Preferences with Kernel Methods
We propose a probabilistic kernel approach for preferential learning fro...
research
∙
05/13/2020
Consistency of permutation tests for HSIC and dHSIC
The Hilbert–Schmidt Independence Criterion (HSIC) is a popular measure o...
research
∙
05/08/2020
Spectral Ranking with Covariates
We consider approaches to the classical problem of establishing a statis...
research
∙
02/13/2020
Bayesian Kernel Two-Sample Testing
In modern data analysis, nonparametric measures of discrepancies between...
research
∙
02/11/2020
Large Scale Tensor Regression using Kernels and Variational Inference
We outline an inherent weakness of tensor factorization models when late...
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...
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...
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...
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...
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...
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...
research
∙
11/26/2018
Rejoinder for "Probabilistic Integration: A Role in Statistical Computation?"
This article is the rejoinder for the paper "Probabilistic Integration: ...
research
∙
10/15/2018
Hyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning...
research
∙
08/01/2018
A Differentially Private Kernel Two-Sample Test
Kernel two-sample testing is a useful statistical tool in determining wh...
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...
research
∙
06/24/2018
A Unified Analysis of Random Fourier Features
We provide the first unified theoretical analysis of supervised learning...
research
∙
05/29/2018
Hamiltonian Variational Auto-Encoder
Variational Auto-Encoders (VAEs) have become very popular techniques to ...
research
∙
05/22/2018
Variational Learning on Aggregate Outputs with Gaussian Processes
While a typical supervised learning framework assumes that the inputs an...
research
∙
04/12/2018
Causal Inference via Kernel Deviance Measures
Discovering the causal structure among a set of variables is a fundament...
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...
research
∙
05/11/2017
Bayesian Approaches to Distribution Regression
Distribution regression has recently attracted much interest as a generi...
research
∙
03/22/2017
