Constraint Active Search for Human-in-the-Loop Optimization with Gustavo Malkomes - #505
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
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Bayesian Optimization and Constraint Active Search
This chapter explores Bayesian optimization and its synergy with surrogate models for solving optimization problems. It introduces constraint active search, detailing its flexibility in dealing with constraints while assessing algorithm performance through metrics like positive points and hypervolume. The discussion emphasizes the necessity of diversity in solutions, highlighting the implications for human-in-the-loop scenarios and design decision-making.
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