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Found 5 papers, 2 papers with code
Nonparametric logistic regression with deep learning
In the logistic regression, we usually consider the maximum likelihood estimator, and the excess risk is the expectation of the Kullback-Leibler (KL) divergence between the true and estimated conditional class probabilities.
Convex Clustering through MM: An Efficient Algorithm to Perform Hierarchical Clustering
Convex clustering is a modern method with both hierarchical and $k$-means clustering characteristics.
More Powerful Selective Kernel Tests for Feature Selection
An approach for addressing this is via conditioning on the selection procedure to account for how we have used the data to generate our hypotheses, and prevent information to be used again after selection.
Fast generalization error bound of deep learning without scale invariance of activation functions
In this paper, using the framework for analyzing the generalization error developed in Suzuki (2018), we derive a fast learning rate for deep neural networks with more general activation functions.
Clustering for high-dimension, low-sample size data using distance vectors
In high-dimension, low-sample size (HDLSS) data, it is not always true that closeness of two objects reflects a hidden cluster structure.
