Bayesian Optimization with Unknown Constraints
This addresses constrained optimization problems in real-world applications where constraints are not known in advance, though it appears incremental as it extends existing Bayesian optimization frameworks to handle noisy and independent constraints.
The paper tackles Bayesian optimization for problems with unknown constraints that may be noisy and independently evaluated, demonstrating effectiveness on three practical examples including optimizing online LDA with topic sparsity constraints, tuning neural networks with memory constraints, and optimizing Hamiltonian Monte Carlo with convergence diagnostics.
Recent work on Bayesian optimization has shown its effectiveness in global optimization of difficult black-box objective functions. Many real-world optimization problems of interest also have constraints which are unknown a priori. In this paper, we study Bayesian optimization for constrained problems in the general case that noise may be present in the constraint functions, and the objective and constraints may be evaluated independently. We provide motivating practical examples, and present a general framework to solve such problems. We demonstrate the effectiveness of our approach on optimizing the performance of online latent Dirichlet allocation subject to topic sparsity constraints, tuning a neural network given test-time memory constraints, and optimizing Hamiltonian Monte Carlo to achieve maximal effectiveness in a fixed time, subject to passing standard convergence diagnostics.