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NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation [article]

Anastasis Kratsios, Cody Hyndman
2021 arXiv   pre-print
This paper introduces a meta-procedure, called Non-Euclidean Upgrading (NEU), which learns feature maps that are expressive enough to embed the universal approximation property (UAP) into most model classes  ...  Effective feature representation is key to the predictive performance of any algorithm.  ...  The authors thank Alina Stancu (Concordia University) for helpful discussions, Josef Teichmann and the entire working group at ETH for their helpful feedback, and Behnoosh Zamanlooy for the Python guidance  ... 
arXiv:1809.00082v4 fatcat:5ue5d7svp5dyhedo7er65ou7cm

Probabilistic Feature Selection in Joint Quantile Time Series Analysis [article]

Ning Ning
2021 arXiv   pre-print
In this paper, we propose a general probabilistic methodology for feature selection in joint quantile time series analysis, under the name of quantile feature selection time series (QFSTS) model.  ...  Quantile feature selection over correlated multivariate time series data has always been a methodological challenge and is an open problem.  ...  Neu: A meta-algorithm for universal uap-invariant feature representation.  ... 
arXiv:2010.01654v2 fatcat:o5bhgjstejgatpcnwjchpflrvq