Optimal Sampling of Parametric Families: Implications for Machine Learning

• Published in: Neural computation Vol. 32; no. 1; pp. 261 - 279
• Main Authors: Huber, Adrian E. G., Anumula, Jithendar, Liu, Shih-Chii
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 01.01.2020; MIT Press Journals, The
• Subjects: Distribution functions; Machine learning; Optimization; Probability distribution functions; Sampling methods; Test sets
• ISSN: 0899-7667; 1530-888X
• DOI: 10.1162/neco_a_01251

It is well known in machine learning that models trained on a training set generated by a probability distribution function perform far worse on test sets generated by a different probability distribution function. In the limit, it is feasible that a continuum of probability distribution functions might have generated the observed test set data; a desirable property of a learned model in that case is its ability to describe most of the probability distribution functions from the continuum equally well. This requirement naturally leads to sampling methods from the continuum of probability distribution functions that lead to the construction of optimal training sets. We study the sequential prediction of Ornstein-Uhlenbeck processes that form a parametric family. We find empirically that a simple deep network trained on optimally constructed training sets using the methods described in this letter can be robust to changes in the test set distribution.