9 Balancing utility and cost with multifidelity optimization
This chapter covers
- The problem of multifidelity optimization with variable cost
- Training a GP on data from multiple sources
- Implementing a cost-aware multifidelity BayesOpt policy
Consider the following questions:
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Should you trust the online reviews saying that the newest season of your favorite TV show isn’t as good as the previous ones and you should quit watching the show, or should you spend your next few weekends watching it to find out for yourself whether you will like the new season?
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After seeing that their neural network model doesn’t perform well after being trained for a few epochs, should an ML engineer cut their losses and switch to a different model, or should they ...
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