Bobak Shahriari

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Big data applications are typically associated with systems involving large numbers of users, massive complex software systems, and large-scale heterogeneous computing and storage architectures. The construction of such systems involves many distributed design choices. The end products (e.g., recommendation systems, medical analysis tools, real-time game(More)
We address the problem of finding the maximizer of a nonlinear function that can only be evaluated, subject to noise, at a finite number of query locations. Further, we will assume that there is a constraint on the total number of permitted function evaluations. We introduce a Bayesian approach for this problem and show that it empirically outperforms both(More)
Bayesian optimization has recently emerged as a powerful and flexible tool in machine learning for hyperparameter tuning and more generally for the efficient global optimization of expensive black box functions. The established practice requires a user-defined bounded domain, which is assumed to contain the global optimizer. However, when little is known(More)
Portfolio methods provide an effective, principled way of combining a collection of acquisition functions in the context of Bayesian optimization. We introduce a novel approach to this problem motivated by an information theoretic consideration. Our construction additionally provides an extension of Thompson sampling to continuous domains with GP priors. We(More)
We address the problem of finding the maximizer of a nonlinear smooth function, that can only be evaluated point-wise, subject to constraints on the number of permitted function evaluations. This problem is also known as fixed-budget best arm identification in the multi-armed bandit literature. We introduce a Bayesian approach for this problem and show that(More)
A recent empirical study highlighted the shocking result that, for many hyperparameter tuning problems, Bayesian optimization methods can be outperformed by random guessing run for twice as many iterations [1]. This is supported by theoretical results showing the optimality of random search under certain assumptions, but disagrees with other theoretical and(More)
We propose a novel framework for the analysis of learning algorithms that allows us to say when such algorithms can and cannot generalize certain patterns from training data to test data. In particular we focus on situations where the rule that must be learned concerns two components of a stimulus being identical. We call such a basis for discrimination an(More)
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