Adaptive selection of Gaussian process kernels and acquisition functions during Bayesian optimization yields modest improvements on synthetic benchmarks and on a solder joint reliability case study.
Are you using test log-likelihood correctly?
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abstract
Test log-likelihood is commonly used to compare different models of the same data or different approximate inference algorithms for fitting the same probabilistic model. We present simple examples demonstrating how comparisons based on test log-likelihood can contradict comparisons according to other objectives. Specifically, our examples show that (i) approximate Bayesian inference algorithms that attain higher test log-likelihoods need not also yield more accurate posterior approximations and (ii) conclusions about forecast accuracy based on test log-likelihood comparisons may not agree with conclusions based on root mean squared error.
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Adaptive Bayesian Data-Driven Design of Reliable Solder Joints for Micro-electronic Devices
Adaptive selection of Gaussian process kernels and acquisition functions during Bayesian optimization yields modest improvements on synthetic benchmarks and on a solder joint reliability case study.