Paper-Conference

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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Scalable Data Assimilation with Message Passing
Data assimilation is a core component of numerical weather prediction systems. The large quantity of data processed during assimilation …
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A Rigorous Link between Deep Ensembles and (Variational) Bayesian Methods
We establish the first mathematically rigorous link between Bayesian, variational Bayesian, and ensemble methods. A key step towards …
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Meta-learning Control Variates: Variance Reduction with Limited Data
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates …
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Multilevel Bayesian Quadrature
Multilevel Monte Carlo is a key tool for approximating integrals involving expensive scientific models. The idea is to use …
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No Train No Gain: Revisiting Efficient Training Algorithms For Transformer-based Language Models
The computation necessary for training Transformer-based language models has skyrocketed in recent years. This trend has motivated …
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Robustifying likelihoods by optimistically re-weighting data
Likelihood-based inferences have been remarkably successful in wide-spanning application areas. However, even after due diligence in …
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