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androbust预测一个对系统至关重要的参数，使其与其他随机变量（r.v.）that either要么affects影响the system系统or being存在affected影响by the system系统.在这里，术语“鲁棒性”意味着可靠性，这是机器学习发展阶段正在进行的第三波浪潮所期望的。设系统变量记为X，关联的可观测量记为Y。这些r.v.中的一个或两个也可以是高维的，而不仅仅是标量。在这篇博士论文中，我将学习函数关系f（.）在X和Y之间，即Y = f（X），以预测X的值，在该值处观察到Y上的（噪声）测试数据，其中预测将是快速和可靠的。

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.