GLASSES: Relieving The Myopia Of Bayesian Optimisation
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Abstract
We present GLASSES: Global optimisation with LookAhead through Stochastic Simulation and Expectedloss Search. The majority of global optimisation approaches in use are myopic, in only considering the impact of the next function value; the nonmyopic approaches that do exist are able to consider only a handful of future evaluations. Our novel algorithm, GLASSES, permits the consideration of dozens of evaluations into the future. This is done by approximating the ideal lookahead loss function, which is expensive to evaluate, by a cheaper alternative in which the future steps of the algorithm are simulated beforehand. An Expectation Propagation algorithm is used to compute the expected value of the loss. We show that the farhorizon planning thus enabled leads to substantive performance gains in empirical tests.


Gonzalez, J., Osborne, M. & Lawrence, N.D.. (). GLASSES: Relieving The Myopia Of Bayesian Optimisation. , in PMLR :790799
