High Dimensional Bayesian Optimization using Dropout

High Dimensional Bayesian Optimization using Dropout

Cheng Li, Sunil Gupta, Santu Rana, Vu Nguyen, Svetha Venkatesh, Alistair Shilton

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence
Main track. Pages 2096-2102. https://doi.org/10.24963/ijcai.2017/291

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 drop-out 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. We demonstrate the efficacy of our algorithms for optimization on two benchmark functions and two real-world applications - training cascade classifiers and optimizing alloy composition.
Keywords:
Machine Learning: Active Learning
Machine Learning: Machine Learning
Constraints and Satisfiability: Constraint Optimisation