Solving Black-Box Optimization Challenge via Learning Search Space Partition for Local Bayesian Optimization
Black-box optimization is one of the vital tasks in machine learning, since it approximates real-world conditions, in that we do not always know all the properties of a given system, up to knowing almost nothing but the results. This paper describes our approach to solving the black-box optimization challenge at NeurIPS 2020 through learning search space partition for local Bayesian optimization. We describe the task of the challenge as well as our algorithm for low budget optimization that we named \texttt{SPBOpt}. We optimize the hyper-parameters of our algorithm for the competition finals using multi-task Bayesian optimization on results from the first two evaluation settings. Our approach has ranked third in the competition finals.
Code (1)
Tasks
Bayesian OptimizationSimilar Papers 제목 키워드 기반
Learning (Re-)Starting Solutions for Vehicle Routing Problems
A key challenge in solving a combinatorial optimization problem is how to guide the agent (i.e., solver) to efficiently explore the enormous search space. Conventional approaches often rely on enumeration (e.g., exhausti…
Combinatorial OptimizationNavigateFast Design Space Exploration of Nonlinear Systems: Part I
System design tools are often only available as input-output blackboxes: for a given design as input they compute an output representing system behavior. Blackboxes are intended to be run in the forward direction. This p…
Active LearningBayesian OptimizationGaussian ProcessesLarge-scale Testing Global Optimization Methods with Black-box Adversarial Attacks
Existing global optimization benchmark suites are of a moderate size and are based on a small number of analytical functions that date back even to the 1970s. This causes a risk of biasing the development of global optim…
Adversarial AttackOPBO: Order-Preserving Bayesian Optimization
Bayesian optimization is an effective method for solving expensive black-box optimization problems. Most existing methods use Gaussian processes (GP) as the surrogate model for approximating the black-box objective funct…
Gaussian ProcessesOptimizing Discrete Spaces via Expensive Evaluations: A Learning to Search Framework
We consider the problem of optimizing expensive black-box functions over discrete spaces (e.g., sets, sequences, graphs). The key challenge is to select a sequence of combinatorial structures to evaluate, in order to ide…