Paper-Conference

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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Vector-Valued Control Variates
Control variates are variance reduction tools for Monte Carlo estimators. They can provide significant variance reduction, but usually …
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Bayesian Numerical Integration with Neural Networks
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: …
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Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum …
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Robust and Scalable Bayesian Online Changepoint Detection
This paper proposes an online, provably robust, and scalable Bayesian approach for changepoint detection. The resulting algorithm has …
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