Peter Frazier - Knowledge-Gradient Methods for Grey-Box Bayesian Optimization
Channel: Secondmind
Date: 2021-04-16

Abstract: The knowledge gradient (KG) is a class of Bayesian optimization acquisition functions that unlocks significant value and flexibility. While expected improvement is effective for standard Bayesian optimization, its guidance becomes questionable when we can measure quantities that don't directly create improvement but are obviously quite informative. Informative quantities undervalued by expected improvement include gradients, biased low-fidelity fast approximations of the objective function, and context variables that are unknown until measured. We provide an introduction to the KG acquisition function focusing on how it can be used in these more exotic Bayesian optimization settings. We then show how KG can be implemented easily to leverage GPU resources within BoTorch using the one-shot KG approach.

Links to relevant papers: https://arxiv.org/pdf/1910.06403.pdf (BOTORCH: Bayesian Optimization in PyTorch), https://arxiv.org/abs/1603.00389 (Multi-Information Source Optimization, NeurIPS 2017), https://arxiv.org/abs/1903.04703 (Practical Multi-fidelity Bayesian Optimization for Hyperparameter Tuning, UAI 2019)

Speaker: Peter Frazier is an Associate Professor of Operations & Information Engineering at Cornell University, and Staff Data Scientist at Uber. You can find more info on his website: https://people.orie.cornell.edu/pfrazier/
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This talk was given at Secondmind Labs, as a part of our (virtual) research seminar. Our research seminar is where we exchange ideas with guest speakers, keeping you up to date with the latest developments and inspiring research topics. Occasionally, Secondmind researchers present their own work as well. You can find a complete list of speakers at https://www.secondmind.ai/labs/seminars/. Learn more about Secondmind Labs at https://www.secondmind.ai/labs/​