In this thesis we devise a method for performing distribution regression utilizing an extension of mixture density networks. This method will be benchmarked against other state-of-the-art methods (utilizing negative log loss) and distribution-agnostic models of similar complexity. We demonstrate that our proposed method performs at least as well as the state-of-the-art methods in distribution regression. Additionally, we show that adding distribution predictive capabilities does not signiﬁcantly decrease performance in models. We illustrate how one can utilize this methodology to regress to conﬁdence interval predictions. Finally, we demonstrate a variety of novel and interesting applications of our framework.
Library of Congress Subject Headings
Regression analysis; Distribution (Probability theory); Machine learning; Uncertainty--Mathematical models
Computer Science (MS)
Department, Program, or Center
Computer Science (GCCIS)
Wilkins, Nicholas, "Efficient Distribution Regression" (2019). Thesis. Rochester Institute of Technology. Accessed from
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