Efficient Gaussian Process Regression for Large Data Sets — mentioned by mxwsn

Sun, 22 Jul 2018 16:13:19 GMT

I consider GPs in two ways: hyperparameter optimization (where GPs are a specific instantiation of bayesian optimization) and a powerful non-linear regression method that supports uncertainty via the rich posterior distributions you get with GPs. However, on the side of hyperparameter optimization, an experiment comparing bayesian optimization to random search with only double the computational resources [0] suggests that BO is not at least 2x better than random search which, to me, means that in practice BO isn't my first go-to for hyperparameter optimization. On the side of regression, its…

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Efficient Gaussian Process Regression for Large Data Sets · HackerPapers discussions

Efficient Gaussian Process Regression for Large Data Sets — mentioned by mxwsn