Paper-Conference

Robust Bayesian Optimisation with Unbounded Corruptions
Bayesian Optimization is critically vulnerable to extreme outliers. Existing provably robust methods typically assume a bounded …
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TabMGP: Martingale posterior with TabPFN
Bayesian inference provides principled uncertainty quantification but is often limited by challenges of prior and likelihood …
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Multilevel neural simulation-based inference
Neural simulation-based inference (SBI) is a popular set of methods for Bayesian inference when models are only available in the form …
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Stationary MMD Points
Approximation of a target probability distribution using a finite set of points is a problem of fundamental importance in numerical …
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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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