Model selection in machine learning (ML) is a crucial part of the Bayesian learning procedure. Model choice may impose strong biases on the resulting predictions, which can hinder the performance of methods such as Bayesian neural networks and neural samplers. On the other hand, newly proposed approaches for Bayesian ML exploit features of approximate inference in function space with implicit stochastic processes (a generalization of Gaussian processes). The approach of Sparse Implicit Processes (SIP) is particularly successful in this regard, since it is fully trainable and achieves flexible predictions. Here, we expand on the original experiments to show that SIP is capable of correcting model bias when the data generating mechanism differs strongly from the one implied by the model. We use synthetic datasets to show that SIP is capable of providing predictive distributions that reflect the data better than the exact predictions of the initial, but wrongly assumed model.

翻译：在机器学习中选择模型是巴伊西亚学习程序的一个关键部分。 模型选择可能对由此得出的预测造成强烈的偏差,这可能会妨碍贝伊西亚神经网络和神经采样器等方法的性能。 另一方面,新提议的巴伊西亚ML利用功能空间中隐含随机过程的近似推断特征(Gaussian过程的概括化)开发Bayesian ML的功能空间方法(Gaussian过程的概括化)。在这方面,Sparse 隐性过程(SIP)的方法特别成功,因为它是完全可受训的,并且实现了灵活的预测。 在这里,我们扩展了最初的实验,以表明在数据生成机制与模型所隐含的机制大不相同时,SIP能够纠正模型偏差。 我们使用合成数据集来表明,SIP能够提供比对最初的准确预测更好反映数据的预测,但错误假设模型的预测。