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Approximate Top-k for Increased Parallelism
We present an evaluation of bucketed approximate top-k algorithms. Computing top-k exactly suffers from limited parallelism, because …
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Targeted Separation and Convergence with Kernel Discrepancies
Maximum mean discrepancies (MMDs) like the kernel Stein discrepancy (KSD) have grown central to a wide range of applications, including …
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Predictive performance of power posteriors
We analyse the impact of using tempered likelihoods in the production of posterior predictions. Our findings reveal that once the …
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On the Robustness of Kernel Goodness-of-Fit Tests
Goodness-of-fit testing is often criticized for its lack of practical relevance; since ``all models are wrong’, the null …
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