We demonstrate two sampling procedures assisted by machine learning models via regression and classification. The main objective is the use of a neural network to suggest points likely inside regions of interest, reducing the number of evaluations of time consuming calculations. We compare results from this approach with results from other sampling methods, namely Markov chain Monte Carlo and MultiNest, obtaining results that range from comparably similar to arguably better. In particular, we augment our classifier method with a boosting technique that rapidly increases the efficiency within a few iterations. We show results from our methods applied to a toy model and the type II 2HDM, using 3 and 7 free parameters, respectively. The code used for this paper and instructions are publicly available on the web.

翻译：我们通过回归与分类展示了两种机器学习模型辅助的采样方法。其主要目标是利用神经网络来推荐可能位于感兴趣区域内的采样点，从而减少耗时计算所需的评估次数。我们将此方法与其他采样技术（即马尔可夫链蒙特卡洛与MultiNest）的结果进行比较，所得结果从相当接近到明显更优不等。特别地，我们通过集成提升技术增强了分类器方法，使其在数次迭代内快速提升效率。我们展示了将所提方法应用于玩具模型和II型双希格斯二重态模型的结果，分别使用了3个和7个自由参数。本文所用代码及操作指南已在网络上公开提供。