Fast, Locally Adaptive Inference for Machine Learning in Graphical Models

图形模型是机器学习中的一种强有力的工具，在医疗诊断、自然语言处理、机器人、语音识别和遗传数据分析等领域都有成功的应用。尽管取得了这样的成功，但现代数据集对图形建模框架提出了新的要求，因为模型可能是巨大的，但图形模型中的准确推理是难以处理的。尽管有大量关于近似推理的文献，但我们希望分析的最大数据集与我们能够处理的最大图形模型之间仍然存在着巨大的差距。为了满足这些新应用的挑战，本项目研究了针对机器学习实际应用中出现的大规模图形模型的新的近似推理算法。现有的推理算法很少能够处理具有连续变量的超大模型，而且一些重要的推理算法，如蒙特卡罗技术，根本没有扩展到这样的模型。计算效率高的推理将大大扩大图形建模框架可适用的应用范围。

Graphical models are a powerful tool in machine learning with successful applications in diverse areas such as medical diagnosis, natural language processing, robotics, speech recognition and analysis of genetic data. Despite this success, modern data sets place new demands on the graphical modelling framework, because the models can be enormous, but exact inference in graphical models is intractable. Despite the extensive literature on approximate inference, there is still a huge gap between the largest data sets that we wish to analyse and the largest graphical models that we can handle.In order to meet the challenges of these new applications, this project concerns new approximate inference algorithms for the large-scale graphical models that arise in practical applications of machine learning. Very few existing inference algorithms can handle extremely large models with continuous variables, and important classes of inference algorithms, such as Monte Carlo techniques, have not been scaled to such models at all. Computationally efficient inference would significantly expand the range of applications to which the graphical modelling framework can be applied.