High Dimensional Bayesian Optimization Using Dropout

Understand

Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible.

• Existing methods depend either on limited active variables or the additive form of the objective function.
• We propose a new method for high-dimensional Bayesian optimization, that uses a dropout strategy to optimize only a subset of variables at each iteration.
• We derive theoretical bounds for the regret and show how it can inform the derivation of our algorithm.