Sufficiently Deep Supervised Learning in High-Dimensions & Fast Prediction, with Applications

该研究致力于开发一种新的方法，用于快速和鲁棒地预测对系统至关重要的参数，使其与影响系统或被系统影响的其他随机变量（r.v.）相关。在这里，“鲁棒”一词意味着在机器学习发展阶段的第三波浪潮中所需要的可靠性。将系统变量记为X，将相关的可观察对象记为Y。或者这两个rv也可以是高维的而不仅仅是标量。在这个phd中，我将学习X和Y之间的函数关系f(.)，即Y = f(X)来预测在Y上观察到（有噪声的）测试基准的X的值，这样预测将是快速可靠的。

The research is dedicated to the development of a novel method for a fast androbust prediction of a parameter that is essential to a system such that it isrelated to other random variable(s) (r.v.) that either affects the system orbeing affected by the system. Here, the term, "robust" implies as reliabilitywhich is desired within the on-going 3rd wave of the developmental stage ofMachine Learning. Let the system variable be denoted as X and the associatedobservable as Y . Either, or both these r.v.s could be higher-dimensional aswell rather than being just scalars. In this Ph.D, I will learn the functionalrelationship f(.) between X and Y i.e. Y = f(X) to predict the values of X atwhich a (noisy) test datum on Y is observed, where the prediction will be fastand reliable.