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Found 16 papers, 6 papers with code
A tutorial on learning from preferences and choices with Gaussian Processes
Preference modelling lies at the intersection of economics, decision theory, machine learning and statistics.
Efficient Computation of Counterfactual Bounds
This allows us to compute exact counterfactual bounds via algorithms for credal nets on a subclass of structural causal models.
Learning Choice Functions with Gaussian Processes
We propose a Gaussian Process model to learn choice functions from choice-data.
Efficient probabilistic reconciliation of forecasts for real-valued and count time series
The forecasts for these time series are required to be coherent, that is, to satisfy the constraints given by the hierarchy.
Bounding Counterfactuals under Selection Bias
Causal analysis may be affected by selection bias, which is defined as the systematic exclusion of data from a certain subpopulation.
Probabilistic Reconciliation of Count Time Series
Forecast reconciliation is an important research topic.
Correlated Product of Experts for Sparse Gaussian Process Regression
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering.
Choice functions based multi-objective Bayesian optimisation
In this work we introduce a new framework for multi-objective Bayesian optimisation where the multi-objective functions can only be accessed via choice judgements, such as ``I pick options A, B, C among this set of five options A, B, C, D, E''.
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes
In a recent contribution we showed that SkewGP and probit likelihood are conjugate, which allows us to compute the exact posterior for non-parametric binary classification and preference learning.
An exact kernel framework for spatio-temporal dynamics
A kernel-based framework for spatio-temporal data analysis is introduced that applies in situations when the underlying system dynamics are governed by a dynamic equation.
Preferential Bayesian optimisation with Skew Gaussian Processes
In this paper, we prove that the true posterior distribution of the preference function is a Skew Gaussian Process (SkewGP), with highly skewed pairwise marginals and, thus, show that Laplace's method usually provides a very poor approximation.
Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian processes to big data.
Skew Gaussian Processes for Classification
In spite of their success, GPs have limited use in some applications, for example, in some cases a symmetric distribution with respect to its mean is an unreasonable model.
Recursive Estimation for Sparse Gaussian Process Regression
Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications.
Adaptive Design of Experiments for Conservative Estimation of Excursion Sets
The system is modeled by an expensive-to-evaluate function, such as a computer experiment, and we are interested in its excursion set, i. e. the set of points where the function takes values above or below some prescribed threshold.
Quantifying uncertainties on excursion sets under a Gaussian random field prior
In this setting, the posterior distribution on the objective function gives rise to a posterior distribution on excursion sets.
