2.7k citations · 2.8k across the 15 of their papers we have counts for
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Improving cluster recovery with feature rescaling factors
Renato Cordeiro de Amorim, Vladimir Makarenkov
The data preprocessing stage is crucial in clustering. Features may describe entities using different scales. To rectify this, one usually applies feature normalisation aiming at r…
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar, Farhad Pourpanah, Sadiq Hussain +9
Uncertainty quantification (UQ) plays a pivotal role in reduction of uncertainties during both optimization and decision making processes. It can be applied to solve a variety of r…
XtracTree: a Simple and Effective Method for Regulator Validation of Bagging Methods Used in Retail Banking
Jeremy Charlier, Vladimir Makarenkov
Bootstrap aggregation, known as bagging, is one of the most popular ensemble methods used in machine learning (ML). An ensemble method is a ML method that combines multiple hypothe…
Representation of Reinforcement Learning Policies in Reproducing Kernel Hilbert Spaces
Bogdan Mazoure, Thang Doan, Tianyu Li +4
We propose a general framework for policy representation for reinforcement learning tasks. This framework involves finding a low-dimensional embedding of the policy on a reproducin…