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Julia Grosse

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TMLR Journal 2023 Journal Article

Optimistic Optimization of Gaussian Process Samples

Julia Grosse
Cheng Zhang
Philipp Hennig

Bayesian optimization is a popular formalism for global optimization, but its computational costs limit it to expensive-to-evaluate functions. A competing, computationally more effi- cient, global optimization framework is optimistic optimization, which exploits prior knowl- edge about the geometry of the search space in form of a dissimilarity function. We investi- gate to which degree the conceptual advantages of Bayesian Optimization can be combined with the computational efficiency of optimistic optimization. By mapping the kernel to a dissimilarity, we obtain an optimistic optimization algorithm for the Bayesian Optimization setting with a run-time of up to $O(N log N )$. As a high-level take-away we find that, when using stationary kernels on objectives of low evaluation cost, optimistic optimization can be preferable over Bayesian optimization, while for strongly coupled and parametric models, Bayesian optimization can perform much better, even at low evaluation cost. As a concep- tual takeaway, our results demonstrate that balancing exploration and exploitation under Gaussian process assumptions does not require computing a posterior.

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UAI Conference 2021 Conference Paper

Probabilistic DAG search

Julia Grosse
Cheng Zhang
Philipp Hennig

Exciting contemporary machine learning problems have recently been phrased in the classic formalism of tree search — most famously, the game of Go. Interestingly, the state-space underlying these sequential decision-making problems often posses a more general latent structure than can be captured by a tree. In this work, we develop a probabilistic framework to exploit a search space’s latent structure and thereby share information across the search tree. The method is based on a combination of approximate inference in jointly Gaussian models for the explored part of the problem, and an abstraction for the unexplored part that imposes a reduction of complexity ad hoc. We empirically find our algorithm to compare favorably to existing non-probabilistic alternatives in Tic-Tac-Toe and a feature selection application.

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