4 citations · 10 across the 21 of their papers we have counts for
3 papers · 1 filter
Bayesian Numerical Integration with Neural Networks
Katharina Ott, Michael Tiemann, Philipp Hennig +1
Bayesian probabilistic numerical methods for numerical integration offer significant advantages over their non-Bayesian counterparts: they can encode prior information about the in…
Meta-learning Control Variates: Variance Reduction with Limited Data
Zhuo Sun, Chris J. Oates, François-Xavier Briol
Control variates can be a powerful tool to reduce the variance of Monte Carlo estimators, but constructing effective control variates can be challenging when the number of samples…
Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
Ayush Bharti, Masha Naslidnyk, Oscar Key +2
Likelihood-free inference methods typically make use of a distance between simulated and real data. A common example is the maximum mean discrepancy (MMD), which has previously bee…