Bayesian Optimization in Action

11 Optimizing multiple objectives at the same time

This chapter covers

• The problem of optimizing multiple objectives at the same time
• Training multiple GPs to learn about multiple objectives at the same time
• Jointly optimizing multiple objectives

Every day, we are faced with optimization tradeoffs:

• “This coffee tastes good, but there’s too much sugar.”

• “That shirt looks great, but it’s out of my price range.”

• “The neural network I just trained has a high accuracy, but it is too big and takes too long to train.”

In an attempt to achieve a good performance on some objective, we sacrifice another criterion that’s just as important: a coffee drinker with a sweet tooth might optimize the taste of their coffee while using an unhealthy amount ...