You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A review of single-source unsupervised domain adaptation 
Chapter Title:  Domain discrepancies 
1. Abstract of Cited Paper (BIB001):  WINNER OF THE 2005 DEGROOT PRIZE! This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. 
2. Abstract of Cited Paper (BIB002):  In transfer learning the aim is to solve new learning tasks using fewer examples by using information gained from solving related tasks. Existing transfer learning methods have been used successfully in practice and PAC analysis of these methods have been developed. But the key notion of relatedness between tasks has not yet been defined clearly, which makes it difficult to understand, let alone answer, questions that naturally arise in the context of transfer, such as, how much information to transfer, whether to transfer information, and how to transfer information across tasks. In this paper, we look at transfer learning from the perspective of Algorithmic Information Theory/Kolmogorov complexity theory, and formally solve these problems in the same sense Solomonoff Induction solves the problem of inductive inference. We define universal measures of relatedness between tasks, and use these measures to develop universally optimal Bayesian transfer learning methods. We also derive results in AIT that are interesting by themselves. To address a concern that arises from the theory, we also briefly look at the notion of Kolmogorov complexity of probability measures. Finally, we present a simple practical approximation to the theory to do transfer learning and show that even these are quite effective, allowing us to transfer across tasks that are superficially unrelated. The latter is an experimental feat which has not been achieved before, and thus shows the theory is also useful in constructing practical transfer algorithms. 
3. Abstract of Cited Paper (BIB003):  Discriminative learning methods for classification perform well when training and test data are drawn from the same distribution. Often, however, we have plentiful labeled training data from a source domain but wish to learn a classifier which performs well on a target domain with a different distribution and little or no labeled training data. In this work we investigate two questions. First, under what conditions can a classifier trained from source data be expected to perform well on target data? Second, given a small amount of labeled target data, how should we combine it during training with the large amount of labeled source data to achieve the lowest target error at test time?We address the first question by bounding a classifier’s target error in terms of its source error and the divergence between the two domains. We give a classifier-induced divergence measure that can be estimated from finite, unlabeled samples from the domains. Under the assumption that there exists some hypothesis that performs well in both domains, we show that this quantity together with the empirical source error characterize the target error of a source-trained classifier.We answer the second question by bounding the target error of a model which minimizes a convex combination of the empirical source and target errors. Previous theoretical work has considered minimizing just the source error, just the target error, or weighting instances from the two domains equally. We show how to choose the optimal combination of source and target error as a function of the divergence, the sample sizes of both domains, and the complexity of the hypothesis class. The resulting bound generalizes the previously studied cases and is always at least as tight as a bound which considers minimizing only the target error or an equal weighting of source and target errors. 
4. Abstract of Cited Paper (BIB004):  This paper presents an analysis of importance weighting for learning from finite samples and gives a series of theoretical and algorithmic results. We point out simple cases where importance weighting can fail, which suggests the need for an analysis of the properties of this technique. We then give both upper and lower bounds for generalization with bounded importance weights and, more significantly, give learning guarantees for the more common case of unbounded importance weights under the weak assumption that the second moment is bounded, a condition related to the Renyi divergence of the traning and test distributions. These results are based on a series of novel and general bounds we derive for unbounded loss functions, which are of independent interest. We use these bounds to guide the definition of an alternative reweighting algorithm and report the results of experiments demonstrating its benefits. Finally, we analyze the properties of normalized importance weights which are also commonly used.

Literature Review:
In order to characterize generalization across domains, a measure of domain dissimilarity is necessary. There are many measures to describe differences between probability distributions, such as Kullback-Leibler divergence, total variation distance, the Wasserstein metric, Kolmogorov complexity or the Kolmogorov-Smirnoff statistic BIB003 , BIB001 , BIB002 . We will discuss two measures in more detail, as they will be used later on in the paper. The symmetric difference hypothesis divergence (D H∆H ) takes two classifiers and looks at to what extent they disagree with each other on both domains BIB004 : In this context, it finds the pair of classifiers h, h ′ for which the difference in disagreements between the source and target domain is largest BIB004 ,  . Its value increases as the domains become more dissimilar. Another example is the Rényi divergence  : where α denotes the order of the divergence. For α = 1, the Rényi divergence equals the Kullback-Leibler divergence  . In words, it corresponds to the expected value, with respect to the source distribution, of a power of the ratio of data distributions in each domain BIB005 . The 2-order divergence will be used in Section 3.1 and a variant of it will be used in Section 5.5.