Search Results for author:
Found 12 papers, 6 papers with code
Domain Generalisation via Imprecise Learning
Out-of-distribution (OOD) generalisation is challenging because it involves not only learning from empirical data, but also deciding among various notions of generalisation, e. g., optimising the average-case risk, worst-case risk, or interpolations thereof.
Looping in the Human Collaborative and Explainable Bayesian Optimization
Like many optimizers, Bayesian optimization often falls short of gaining user trust due to opacity.
Causal Strategic Learning with Competitive Selection
We study the problem of agent selection in causal strategic learning under multiple decision makers and address two key challenges that come with it.
Gated Domain Units for Multi-source Domain Generalization
To address this problem, we postulate that real-world distributions are composed of latent Invariant Elementary Distributions (I. E. D) across different domains.
Explaining Preferences with Shapley Values
While preference modelling is becoming one of the pillars of machine learning, the problem of preference explanation remains challenging and underexplored.
Giga-scale Kernel Matrix Vector Multiplication on GPU
Kernel matrix-vector multiplication (KMVM) is a foundational operation in machine learning and scientific computing.
RKHS-SHAP: Shapley Values for Kernel Methods
Feature attribution for kernel methods is often heuristic and not individualised for each prediction.
BayesIMP: Uncertainty Quantification for Causal Data Fusion
While causal models are becoming one of the mainstays of machine learning, the problem of uncertainty quantification in causal inference remains challenging.
Deconditional Downscaling with Gaussian Processes
Yet, when LR samples are modeled as aggregate conditional means of HR samples with respect to a mediating variable that is globally observed, the recovery of the underlying fine-grained field can be framed as taking an "inverse" of the conditional expectation, namely a deconditioning problem.
Kernel-based Graph Learning from Smooth Signals: A Functional Viewpoint
In this paper, we propose a novel graph learning framework that incorporates the node-side and observation-side information, and in particular the covariates that help to explain the dependency structures in graph signals.
Learning Inconsistent Preferences with Gaussian Processes
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 function values.
Spectral Ranking with Covariates
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 covariate information.
