Article-Journal

(De)-regularized Maximum Mean Discrepancy Gradient Flow
We introduce a (de)-regularization of the Maximum Mean Discrepancy (DrMMD) and its Wasserstein gradient flow. Existing gradient flows …
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The Impact of Loss Estimation on Gibbs Measures
In recent years, the shortcomings of Bayes posteriors as inferential devices has received increased attention. A popular strategy for …
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Comparing Scale Parameter Estimators for Gaussian Process Regression: Cross Validation and Maximum Likelihood
Gaussian process (GP) regression is a Bayesian nonparametric method for regression and interpolation, offering a principled way of …
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Discrepancy-based Inference for Intractable Generative Models using Quasi-Monte Carlo
Intractable generative models are models for which the likelihood is unavailable but sampling is possible. Most approaches to parameter …
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Generalized Bayesian Inference for Discrete Intractable Likelihood
Discrete state spaces represent a major computational challenge to statistical inference, since the computation of normalization …
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Robust Generalised Bayesian Inference for Intractable Likelihoods
Generalised Bayesian inference updates prior beliefs using a loss function, rather than a likelihood, and can therefore be used to …
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A General Method for Calibrating Stochastic Radio Channel Models with Kernels
Calibrating stochastic radio channel models to new measurement data is challenging when the likelihood function is intractable. The …
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Stein's Method Meets Computational Statistics: A Review of Some Recent Developments
Stein’s method compares probability distributions through the study of a class of linear operators called Stein operators. While …
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Interlocking Backpropagation: Improving depthwise model-parallelism
The number of parameters in state of the art neural networks has drastically increased in recent years. This surge of interest in large …
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Composite Goodness-of-fit Tests with Kernels
We propose kernel-based hypothesis tests for the challenging composite testing problem, where we are interested in whether the data …
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