Paper-Conference

Kernel Quantile Embeddings and Associated Probability Metrics
Embedding probability distributions into reproducing kernel Hilbert spaces (RKHS) has enabled powerful nonparametric methods such as …
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Prediction-Centric Uncertainty Quantification via MMD
Deterministic mathematical models, such as those specified via differential equations, are a powerful tool to communicate scientific …
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Nested Expectations with kernel Quadrature
This paper considers the challenging computational task of estimating nested expectations. Existing algorithms, such as nested Monte …
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Cost-aware Simulation-based Inference
Simulation-based inference (SBI) is the preferred framework for estimating parameters of intractable models in science and engineering. …
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Robust and Conjugate Spatio-Temporal Gaussian Processes
State-space formulations allow for Gaussian process (GP) regression with linear-in-time computational cost in spatio-temporal settings, …
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Decision Making under the Exponential Family: Distributionally Robust Optimisation with Bayesian Ambiguity Sets
Decision making under uncertainty is challenging as the data-generating process (DGP) is often unknown. Bayesian inference proceeds by …
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Conditional Bayesian Quadrature
We propose a novel approach for estimating conditional or parametric expectations in the setting where obtaining samples or evaluating …
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Conformal Counterfactual Inference under Hidden Confounding
Personalized decision making requires the knowledge of potential outcomes under different treatments, and confidence intervals about …
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Outlier-robust Kalman Filtering through Generalised Bayes
We derive a novel, provably robust, and closed-form Bayesian update rule for online filtering in state-space models in the presence of …
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Robust and Conjugate Gaussian Process Regression
To enable closed form conditioning, a common assumption in Gaussian process (GP) regression is independent and identically distributed …
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