Title: BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering Show Chinese title

Title: BOASF：通过自适应连续过滤加速自动机器学习的统一框架

Abstract: Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning task successfully and efficiently. Finding the optimal machine learning model or the hyperparameter combination set from a large number of possible alternatives usually requires considerable expert knowledge and experience. To tackle this problem, we propose a combined Bayesian Optimization and Adaptive Successive Filtering algorithm (BOASF) under a unified multi-armed bandit framework to automate the model selection or the hyperparameter optimization. Specifically, BOASF consists of multiple evaluation rounds in each of which we select promising configurations for each arm using the Bayesian optimization. Then, ASF can early discard the poor-performed arms adaptively using a Gaussian UCB-based probabilistic model. Furthermore, a Softmax model is employed to adaptively allocate available resources for each promising arm that advances to the next round. The arm with a higher probability of advancing will be allocated more resources. Experimental results show that BOASF is effective for speeding up the model selection and hyperparameter optimization processes while achieving robust and better prediction performance than the existing state-of-the-art automatic machine learning methods. Moreover, BOASF achieves better anytime performance under various time budgets. Show Chinese abstract

Abstract: 机器学习在许多应用领域取得了巨大成功。 然而，对于非专家实践者来说，成功且高效地解决机器学习任务始终是非常具有挑战性的。 从大量可能的替代方案中找到最优的机器学习模型或超参数组合集通常需要大量的专业知识和经验。 为了解决这个问题，我们提出了一种在统一多臂老虎机框架下的贝叶斯优化和自适应连续过滤算法（BOASF），以自动化模型选择或超参数优化。 具体来说，BOASF在每个评估轮次中使用贝叶斯优化为每个臂选择有前景的配置。 然后， ASF 可以使用基于高斯UCB的概率模型自适应地提前丢弃表现不佳的臂。 此外，采用 Softmax 模型来为进入下一轮的每个有前景的臂自适应地分配可用资源。 具有更高推进概率的臂将被分配更多的资源。 实验结果表明，BOASF在加速模型选择和超参数优化过程方面是有效的，并且在实现稳健且优于现有最先进的自动机器学习方法的预测性能方面表现更好。 此外，BOASF在各种时间预算下实现了更好的随时性能。