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Found 4 papers, 3 papers with code
NLS: an accurate and yet easy-to-interpret regression method
We develop NLS (neural local smoother), a method that is complex enough to give good predictions, and yet gives solutions that are easy to be interpreted without the need of using a separate interpreter.
Distance Assessment and Hypothesis Testing of High-Dimensional Samples using Variational Autoencoders
Given two distinct datasets, an important question is if they have arisen from the the same data generating function or alternatively how their data generating functions diverge from one another.
Conditional independence testing: a predictive perspective
Conditional independence testing is a key problem required by many machine learning and statistics tools.
The NN-Stacking: Feature weighted linear stacking through neural networks
We show that while our approach keeps the interpretative features of Breiman's method at a local level, it leads to better predictive power, especially in datasets with large sample sizes.
