In this tutorial paper, we first define mean squared error, variance, covariance, and bias of
both random variables and classification/predictor models. Then, we formulate the true and …

This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial
autoencoders, and their variants. We start with explaining adversarial learning and the …

Dimensionality reduction, also known as manifold learning, is an area of machine learning
used for extracting informative features from data for better representation of data or …

This is a tutorial and survey paper on factor analysis, probabilistic Principal Component
Analysis (PCA), variational inference, and Variational Autoencoder (VAE). These methods …

This is a tutorial and survey paper on Boltzmann Machine (BM), Restricted Boltzmann
Machine (RBM), and Deep Belief Network (DBN). We start with the required background on …

Data representativity is crucial when drawing inference from data through machine learning
models. Scholars have increased focus on unraveling the bias and fairness in models, also …

This is a tutorial and survey paper on metric learning. Algorithms are divided into spectral,
probabilistic, and deep metric learning. We first start with the definition of distance metric …

This survey is focused on certain sequential decision-making problems that involve
optimizing over probability functions. We discuss the relevance of these problems for …

In practical applications, there are usually complex stochastic and vague uncertainties
caused by inherent randomness and unknown dynamics. How to model uncertainty and …

It was mentioned in Chap. 11 that metric learning can be divided into spectral, probabilistic,
and deep metric learning. Chapters 11 and 13 explained that both spectral and probabilistic …