Henry Moss - MUMBO: MUlti-task Max-value Bayesian Optimization
| Dates: | 5 November 2019 |
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| Times: | 12:00 - 13:00 |
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| What is it: | Seminar |
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| Organiser: | Department of Mathematics |
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| Who is it for: | University staff, External researchers, Current University students |
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| Speaker: | Henry Moss |
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Join us for this research seminar, part of the SQUIDS (Statistics, quantification of uncertainty, inverse problems and data science) seminar series.
Abstract: We propose MUMBO, the first high-performing yet computationally efficient acquisition function for multi-task Bayesian optimization. Here, the challenge is to perform efficient optimization by evaluating low-cost functions somehow related to our true target function, a broad class of problems including the popular task of multi-fidelity optimization. However, while information-theoretic acquisition functions are known to provide state-of-the-art Bayesian optimization, existing implementations for multi-task scenarios have prohibitive computational requirements. Previous acquisition functions have therefore been suitable only for problems with both low-dimensional parameter spaces and function query costs sufficiently large to overshadow very significant optimization overheads. In this work, we derive a novel multi-task version of the max-value entropy search of Wang et al 2017, delivering low-cost and robust performance across classic optimization challenges and multi-task hyper-parameter tuning tasks. Our approach is scalable and efficient, allowing multi-task Bayesian optimization to be deployed in problems with rich parameter and fidelity spaces.
Speaker
Henry Moss
Organisation: Lancaster University
Travel and Contact Information
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Frank Adams 1
Alan Turing Building
Manchester
