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Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang · Daniel Jiang · Maximilian Balandat · Brian Karrer · Jacob Gardner · Roman Garnett

Tue Dec 08 09:00 PM -- 11:00 PM (PST) @ Poster Session 2 #663

Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a highly intractable stochastic dynamic program. Myopic approaches, such as expected improvement, are often adopted in practice, but they ignore the long-term impact of the immediate decision. Existing nonmyopic approaches are mostly heuristic and/or computationally expensive. In this paper, we provide the first efficient implementation of general multi-step lookahead Bayesian optimization, formulated as a sequence of nested optimization problems within a multi-step scenario tree. Instead of solving these problems in a nested way, we equivalently optimize all decision variables in the full tree jointly, in a "one-shot" fashion. Combining this with an efficient method for implementing multi-step Gaussian process "fantasization," we demonstrate that multi-step expected improvement is computationally tractable and exhibits performance superior to existing methods on a wide range of benchmarks.

Author Information

Shali Jiang (Facebook)
Daniel Jiang (Facebook)
Max Balandat (Facebook)
Brian Karrer (Facebook)
Jacob Gardner (University of Pennsylvania)
Roman Garnett (Washington University in St. Louis)

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