A recent thread about practical use of GPs reminded me that I haven’t posted about the 5 BayesOpt-related papers the Adaptive Experimentation team at Facebook is presenting at NeurIPS 2020 this year. The methods from these papers are all being applied to real-world problems.
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Besides examples in the paper, one-shot KG performs better than qNEI, which tends to perform better than implementations f EI we've tested. KG is particularly useful for multi-fidelity optimization, for which we see great performance on AutoML and simulation optimization tasks.
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Thanks. I’m coming from a physics background, so found myself getting a bit lost in section 4.2 of the paper. But I’m really excited to try out BoTORCH!
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