971 citations · 3k across the 74 of their papers we have counts for
42 papers · 1 filter
Credal Two-Sample Tests of Epistemic Uncertainty
Siu Lun Chau, Antonin Schrab, Arthur Gretton +2
We introduce credal two-sample testing, a new hypothesis testing framework for comparing credal sets -- convex sets of probability measures where each element captures aleatoric un…
Explaining the Uncertain: Stochastic Shapley Values for Gaussian Process Models
Siu Lun Chau, Krikamol Muandet, Dino Sejdinovic
We present a novel approach for explaining Gaussian processes (GPs) that can utilize the full analytical covariance structure present in GPs. Our method is based on the popular sol…
A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
Veit David Wild, Sahra Ghalebikesabi, Dino Sejdinovic +1
We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods. A key step towards this it to reformulate the non-convex optimisat…
Returning The Favour: When Regression Benefits From Probabilistic Causal Knowledge
Shahine Bouabid, Jake Fawkes, Dino Sejdinovic
A directed acyclic graph (DAG) provides valuable prior knowledge that is often discarded in regression tasks in machine learning. We show that the independences arising from the pr…
Doubly Robust Kernel Statistics for Testing Distributional Treatment Effects
Jake Fawkes, Robert Hu, Robin J. Evans +1
With the widespread application of causal inference, it is increasingly important to have tools which can test for the presence of causal effects in a diverse array of circumstance…
Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation
Diego Martinez-Taboada, Dino Sejdinovic
The counterfactual distribution models the effect of the treatment in the untreated group. While most of the work focuses on the expected values of the treatment effect, one may be…
Sequential Decision Making on Unmatched Data using Bayesian Kernel Embeddings
Diego Martinez-Taboada, Dino Sejdinovic
The problem of sequentially maximizing the expectation of a function seeks to maximize the expected value of a function of interest without having direct control on its features. I…
Explaining Preferences with Shapley Values
Robert Hu, Siu Lun Chau, Jaime Ferrando Huertas +1
While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored. In this paper, we propos…
Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning
Veit D. Wild, Robert Hu, Dino Sejdinovic
We develop a framework for generalized variational inference in infinite-dimensional function spaces and use it to construct a method termed Gaussian Wasserstein inference (GWI). G…
Selection, Ignorability and Challenges With Causal Fairness
Jake Fawkes, Robin Evans, Dino Sejdinovic
In this paper we look at popular fairness methods that use causal counterfactuals. These methods capture the intuitive notion that a prediction is fair if it coincides with the pre…
RKHS-SHAP: Shapley Values for Kernel Methods
Siu Lun Chau, Robert Hu, Javier Gonzalez +1
Feature attribution for kernel methods is often heuristic and not individualised for each prediction. To address this, we turn to the concept of Shapley values~(SV), a coalition ga…
BayesIMP: Uncertainty Quantification for Causal Data Fusion
Siu Lun Chau, Jean-François Ton, Javier González +2
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging. In this paper, we stud…
Connections and Equivalences between the Nyström Method and Sparse Variational Gaussian Processes
Veit Wild, Motonobu Kanagawa, Dino Sejdinovic
We investigate the connections between sparse approximation methods for making kernel methods and Gaussian processes (GPs) scalable to large-scale data, focusing on the Nyström met…
Survival Regression with Proper Scoring Rules and Monotonic Neural Networks
David Rindt, Robert Hu, David Steinsaltz +1
We consider frequently used scoring rules for right-censored survival regression models such as time-dependent concordance, survival-CRPS, integrated Brier score and integrated bin…
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
Xingyue Pu, Siu Lun Chau, Xiaowen Dong +1
The problem of graph learning concerns the construction of an explicit topological structure revealing the relationship between nodes representing data entities, which plays an inc…
Benign Overfitting and Noisy Features
Zhu Li, Weijie Su, Dino Sejdinovic
Modern machine learning often operates in the regime where the number of parameters is much higher than the number of data points, with zero training loss and yet good generalizati…
Variational Inference with Continuously-Indexed Normalizing Flows
Anthony Caterini, Rob Cornish, Dino Sejdinovic +1
Continuously-indexed flows (CIFs) have recently achieved improvements over baseline normalizing flows on a variety of density estimation tasks. CIFs do not possess a closed-form ma…
Meta Learning for Causal Direction
Jean-Francois Ton, Dino Sejdinovic, Kenji Fukumizu
The inaccessibility of controlled randomized trials due to inherent constraints in many fields of science has been a fundamental issue in causal inference. In this paper, we focus…
Learning Inconsistent Preferences with Gaussian Processes
Siu Lun Chau, Javier González, Dino Sejdinovic
We revisit widely used preferential Gaussian processes by Chu et al.(2005) and challenge their modelling assumption that imposes rankability of data items via latent utility functi…
Spectral Ranking with Covariates
Siu Lun Chau, Mihai Cucuringu, Dino Sejdinovic
We consider spectral approaches to the problem of ranking n players given their incomplete and noisy pairwise comparisons, but revisit this classical problem in light of player cov…
Large Scale Tensor Regression using Kernels and Variational Inference
Robert Hu, Geoff K. Nicholls, Dino Sejdinovic
We outline an inherent weakness of tensor factorization models when latent factors are expressed as a function of side information and propose a novel method to mitigate this weakn…
Kernel Dependence Regularizers and Gaussian Processes with Applications to Algorithmic Fairness
Zhu Li, Adrian Perez-Suay, Gustau Camps-Valls +1
Current adoption of machine learning in industrial, societal and economical activities has raised concerns about the fairness, equity and ethics of automated decisions. Predictive…
Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings
Jean-Francois Ton, Lucian Chan, Yee Whye Teh +1
Current meta-learning approaches focus on learning functional representations of relationships between variables, i.e. on estimating conditional expectations in regression. In many…
Hyperparameter Learning via Distributional Transfer
Ho Chung Leon Law, Peilin Zhao, Lucian Chan +2
Bayesian optimisation is a popular technique for hyperparameter learning but typically requires initial exploration even in cases where similar prior tasks have been solved. We pro…