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Bayesian optimization (BO) is a model-based approach for gradient-freeblack-box function optimization. Typically, BO is powered by a Gaussian process(GP), whose algorithmic complexity is cubic in the number of evaluations.Hence, GP-based BO cannot leverage large amounts of past or related functionevaluations, for example, to warm start the BO procedure. We develop a multipleadaptive Bayesian linear regression model as a scalable alternative whosecomplexity is linear in the number of observations. The multiple Bayesianlinear regression models are coupled through a shared feedforward neuralnetwork, which learns a joint representation and transfers knowledge acrossmachine learning problems.

Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian Optimization with Warm Start