A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning — mentioned by abhgh

Sun, 22 Jul 2018 19:42:50 GMT

One of their more interesting applications is in Bayesian Optimization (BO). You'd do BO optimization where evaluating the objective function is expensive. As against, lets say, where you might use gradient descent, where you navigate your space via repeated evaluation of the objective function. BO essentially starts building its own model of what the objective function looks like in relation to its parameters, and picks a few promising points to evaluate it at. Thus, it moves the burden of evaluating a costly objective multiple times to identifying these highly promising points. A BO…

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A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning · HackerPapers discussions

A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning — mentioned by abhgh