7,484 research outputs found
An Entropy Search Portfolio for Bayesian Optimization
Bayesian optimization is a sample-efficient method for black-box global
optimization. How- ever, the performance of a Bayesian optimization method very
much depends on its exploration strategy, i.e. the choice of acquisition
function, and it is not clear a priori which choice will result in superior
performance. While portfolio methods provide an effective, principled way of
combining a collection of acquisition functions, they are often based on
measures of past performance which can be misleading. To address this issue, we
introduce the Entropy Search Portfolio (ESP): a novel approach to portfolio
construction which is motivated by information theoretic considerations. We
show that ESP outperforms existing portfolio methods on several real and
synthetic problems, including geostatistical datasets and simulated control
tasks. We not only show that ESP is able to offer performance as good as the
best, but unknown, acquisition function, but surprisingly it often gives better
performance. Finally, over a wide range of conditions we find that ESP is
robust to the inclusion of poor acquisition functions.Comment: 10 pages, 5 figure
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Bayesian optimization usually assumes that a Bayesian prior is given.
However, the strong theoretical guarantees in Bayesian optimization are often
regrettably compromised in practice because of unknown parameters in the prior.
In this paper, we adopt a variant of empirical Bayes and show that, by
estimating the Gaussian process prior from offline data sampled from the same
prior and constructing unbiased estimators of the posterior, variants of both
GP-UCB and probability of improvement achieve a near-zero regret bound, which
decreases to a constant proportional to the observational noise as the number
of offline data and the number of online evaluations increase. Empirically, we
have verified our approach on challenging simulated robotic problems featuring
task and motion planning.Comment: Proceedings of the Thirty-second Conference on Neural Information
Processing Systems, 201
Hyperparameter Learning via Distributional Transfer
Bayesian optimisation is a popular technique for hyperparameter learning but
typically requires initial exploration even in cases where similar prior tasks
have been solved. We propose to transfer information across tasks using learnt
representations of training datasets used in those tasks. This results in a
joint Gaussian process model on hyperparameters and data representations.
Representations make use of the framework of distribution embeddings into
reproducing kernel Hilbert spaces. The developed method has a faster
convergence compared to existing baselines, in some cases requiring only a few
evaluations of the target objective
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