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INFORMS TutORial: Bayesian Optimization

By Peter Frazier | Bayesian optimization is widely used for tuning deep neural networks and optimizing other black-box objective functions that take a long time to evaluate. In this tutorial, we describe how Bayesian optimization works, including the Bayesian machine learning model it uses to model the objective function, Gaussian process regression, and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. We then describe applications at Yelp and Uber, explain techniques important for making it work well in practice, and survey techniques for solving "exotic" Bayesian optimization problems. INFORMS is the largest professional association for the decision and data sciences. Welcome! We are home to a diverse collection of academic and industry experts in fields including operations research, analytics, management science, economics, behavioral science, statistics, artificial intelligence, data science, applied mathematics, and more. Although our members' work is often highly complex, we are unified under a simple, shared mission: advance and promote the science and technology of decision making to save lives, save money, and solve problems. Whether you're at the beginning of your academic and professional adventure or decades into a successful career, INFORMS membership will connect you with a group of peers with whom you can collaborate, learn, grow, and share your journey. ⭐️Join INFORMS: https://bit.ly/3BkR66X ⭐️YouTube channel: https://bit.ly/3zeAsEl Social Media: ➡️Instagram:   / informs_orms   ➡️Twitter:   / informs   ➡️LinkedIn:   / info.  . ➡️Facebook:   / informspage