论文标题
可扩展的组合贝叶斯优化，具有可拖动的统计模型
Scalable Combinatorial Bayesian Optimization with Tractable Statistical models
论文作者
论文摘要
我们研究了优化昂贵的黑框功能在组合空间（例如集合，序列，树和图）上的问题。 BOC（Baptista and Poloczek，2018）是一种最先进的贝叶斯优化方法，用于可拖动的统计模型，该方法执行基于半准编程的采集功能优化（AFO），以选择用于评估的下一个结构。不幸的是，对于大量的二进制和/或分类变量，BOC的尺度很差。根据解决二进制二元二元计划的最新进展（ITO和Fujimaki，2016年），我们研究了一种称为参数化的下义松弛（PSR）的方法，目的是提高BOCS模型解决AFO问题的可扩展性和准确性。 PSR方法取决于两个关键想法。首先，将AFO问题重新制定为一些未知参数，可以使用最小图切割算法有效地解决。其次，构建优化问题，以估算未知参数，并与真实目标紧密近似。关于BOCS模型的PSR的不同基准问题的实验显示了PSR的显着改善。源代码可从https://github.com/aryandeshwal/submodular_relaxation_bocs获得。
We study the problem of optimizing expensive blackbox functions over combinatorial spaces (e.g., sets, sequences, trees, and graphs). BOCS (Baptista and Poloczek, 2018) is a state-of-the-art Bayesian optimization method for tractable statistical models, which performs semi-definite programming based acquisition function optimization (AFO) to select the next structure for evaluation. Unfortunately, BOCS scales poorly for large number of binary and/or categorical variables. Based on recent advances in submodular relaxation (Ito and Fujimaki, 2016) for solving Binary Quadratic Programs, we study an approach referred as Parametrized Submodular Relaxation (PSR) towards the goal of improving the scalability and accuracy of solving AFO problems for BOCS model. PSR approach relies on two key ideas. First, reformulation of AFO problem as submodular relaxation with some unknown parameters, which can be solved efficiently using minimum graph cut algorithms. Second, construction of an optimization problem to estimate the unknown parameters with close approximation to the true objective. Experiments on diverse benchmark problems show significant improvements with PSR for BOCS model. The source code is available at https://github.com/aryandeshwal/Submodular_Relaxation_BOCS .
