Single domain generalization aims to develop a robust model trained on a source domain to generalize well on unseen target domains. Recent progress in single domain generalization has focused on expanding the scope of training data through style (e.g., backgrounds) augmentation. However, existing methods are difficult to generate data with large style shifts due to the lack of precise correlation measures between the generated and original data, and they struggle to effectively capture the consistency between the generated and original data when learning feature representations. In this article, we propose a novel Mutual Information-guided Style Augmentation (MISA) based single domain generalization method. Specifically, MISA incorporates a style diversity module, which uses the matrix-based Rényi’s \(\alpha\) -order entropy functionals to compute an approximate mutual information value between the augmented and original data, minimizing it to guide style generator learning. Moreover, MISA combines the merits of the random convolution and affine transformation to further improve the texture diversity of the augmented data. Additionally, MISA introduces a representation learning module, which minimizes the approximate mutual information value between the prediction logits of the original sample and its corresponding residual component to capture the consistency between the generated and original data for feature representation optimization. Using five real-world datasets, the extensive experiments have demonstrated the effectiveness of MISA, in comparison with state-of-the-art methods.

Single domain generalization aims to learn a model that generalizes well to unseen target domains by using a related source domain. However, most existing methods only focus on improving the generalization performance of the model during training, making it difficult to achieve satisfactory performance when deployed in the target domain with large domain shifts. In this paper, we propose a Practical Single Domain Generalization (PSDG) method, which first leverages the knowledge in a source domain to establish a model with good generalization ability in the training phase, and subsequently updates the model to adapt to target domain data using knowledge in the unlabeled target domain during the testing phase. Specifically, during training, PSDG leverages a newly proposed style (e.g., background features) generator named StyIN to generate novel domain data. Moreover, PSDG introduces style-diversity regularization to constantly synthesize distinct styles to expand the coverage of training data, and introduces object-consistency regularization to capture consistency between the currently generated data and the original data, making the model filter style knowledge during training. During testing, PSDG uses a sample-aware and sharpness-aware minimization method to seek for a flat entropy minimum surface for further model optimization by using the knowledge in the unlabeled target domain. Using three real-world datasets the experiments have demonstrated the effectiveness of PSDG, in comparison with several state-of-the-art methods.

Almost all existing causal feature selection methods are proposed without considering the problem of sample selection bias. However, in practice, as data-gathering process cannot be fully controlled, sample selection bias often occurs, leading to spurious correlations between features and the class variable, which seriously deteriorates the performance of those existing methods. In this article, we study the problem of causal feature selection under sample selection bias and propose a novel Progressive Causal Feature Selection (PCFS) algorithm which has three phases. First, PCFS learns the sample weights to balance the treated group and control group distributions corresponding to each feature for removing spurious correlations. Second, based on the sample weights, PCFS uses a weighted cross-entropy model to estimate the causal effect of each feature and removes some irrelevant features from the confounder set. Third, PCFS progressively repeats the first two phases to remove more irrelevant features and finally obtains a causal feature set. Using synthetic and real-world datasets, the experiments have validated the effectiveness of PCFS, in comparison with several state-of-the-art classical and causal feature selection methods.

In this study, we investigate a challenging problem, namely, robust domain adaptation, where data from only a single well-labeled source domain are available in the training phase. To address this problem, assuming that the causal relationships between the features and the class variable are robust across domains, we propose a novel causal autoencoder (CAE), which integrates a deep autoencoder and a causal structure learning model to learn causal representations using data from a single source domain. Specifically, a deep autoencoder model is adopted to learn the low-dimensional representations, and a causal structure learning model is designed to separate the low-dimensional representations into two groups: causal representations and task-irrelevant representations. Using three real-world datasets, the experiments have validated the effectiveness of CAE, in comparison with eleven state-of-the-art methods.

Learning a causal structure from observational data is crucial for data scientists. Recent advances in causal structure learning (CSL) have focused on local-to-global learning, since the local-to-global CSL can be scaled to high-dimensional data. The local-to-global CSL algorithms first learn the local skeletons, then construct the global skeleton, and finally orient edges. In practice, the performance of local-to-global CSL mainly depends on the accuracy of the global skeleton. However, in many real-world settings, owing to inevitable data quality issues (e.g. noise and small sample), existing local-to-global CSL methods often yield many asymmetric edges (e.g., given anasymmetric edge containing variables A and B, the learned skeleton of A contains B, but the learned skeleton of B does not contain A), which make it difficult to construct a high quality global skeleton. To tackle this problem, this paper proposes a Bootstrap sampling based Causal Structure Learning (BCSL) algorithm. The novel contribution of BCSL is that it proposes an integrated global skeleton learning strategy that can construct more accurate global skeletons. Specifically, this strategy first utilizes the Bootstrap method to generate multiple sub-datasets, then learns the local skeleton of variables on each asymmetric edge on those sub-datasets, and finally designs a novel scoring function to estimate the learning results on all sub-datasets for correcting the asymmetric edge. Extensive experiments on both benchmark and real datasets verify the effectiveness of the proposed method.