Paper-Conference

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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Towards Healing the Blindness of Score Matching
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a …
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Robust Bayesian inference for simulator-based models via the MMD posterior bootstrap
Simulator-based models are models for which the likelihood is intractable but simulation of synthetic data is possible. They are often …
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