A body of work has been done to automate machine learning algorithm to highlight the importance of model choice. Automating the process of choosing the best forecasting model and its corresponding parameters can result to improve a wide range of real-world applications. Bayesian optimisation (BO) uses a blackbox optimisation methods to propose solutions according to an exploration-exploitation trade-off criterion through acquisition functions. BO framework imposes two key ingredients: a probabilistic surrogate model that consist of prior belief of the unknown objective function(data-dependant) and an objective function that describes how optimal is the model-fit. Choosing the best model and its associated hyperparameters can be very expensive, and is typically fit using Gaussian processes (GPs) and at some extends applying approximate inference due its intractability. However, since GPs scale cubically with the number of observations, it has been challenging to handle objectives whose optimization requires many evaluations. In addition, most real-dataset are non-stationary which make idealistic assumptions on surrogate models. The necessity to solve the analytical tractability and the computational feasibility in a stochastic fashion enables to ensure the efficiency and the applicability of Bayesian optimisation. In this paper we explore the use of neural networks as an alternative to GPs to model distributions over functions, we provide a link between density-ratio estimation and class probability estimation based on approximate inference, this reformulation provides algorithm efficiency and tractability.

翻译：已经做了大量工作,将机器学习算法自动化,以突出模型选择的重要性。 自动选择最佳预测模型及其相应参数的过程可以导致改进一系列广泛的真实世界应用。 巴伊西亚优化(BO)使用黑盒优化方法,根据勘探-开发交易标准,通过获取功能提出解决方案。 BO框架规定了两个关键要素: 概率替代模型,它包括先前对未知目标功能(数据依赖性)的信念,以及描述模型适合程度的客观功能(数据依赖性)的客观功能。 选择最佳模型及其相关超参数的过程可能非常昂贵,而且通常适合使用高山流程(GP),有些则由于其易受吸引性而扩大应用近似推推推法。 但是,由于GPS的比重与观察数量不相仿,因此很难处理其优化需要许多评价的目标。 此外,大多数真实数据集不是固定的,对模型进行理想化的假设。 有必要解决分析性可变性及其相关的超标值模型的可选性, 从而使得分析性可选性、可选性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性能、可选择性、可使可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、可选择性、