As AI systems have obtained significant performance to be deployed widely in our daily lives and human society, people both enjoy the benefits brought by these technologies and suffer many social issues induced by these systems. To make AI systems good enough and trustworthy, plenty of researches have been done to build guidelines for trustworthy AI systems. Machine learning is one of the most important parts of AI systems, and representation learning is the fundamental technology in machine learning. How to make representation learning trustworthy in real-world application, e.g., cross domain scenarios, is very valuable and necessary for both machine learning and AI system fields. Inspired by the concepts in trustworthy AI, we proposed the first trustworthy representation learning across domains framework, which includes four concepts, i.e., robustness, privacy, fairness, and explainability, to give a comprehensive literature review on this research direction. Specifically, we first introduce the details of the proposed trustworthy framework for representation learning across domains. Second, we provide basic notions and comprehensively summarize existing methods for the trustworthy framework from four concepts. Finally, we conclude this survey with insights and discussions on future research directions.

Short text clustering is a challenging unsupervised learning task which requires a complex representation of each document to effectively model the semantics and syntactic structure of the text. Existing works have attempted to tackle this challenging task by incorporating additional information to the model, such as number of clusters, number of datapoints in each clusters, the distribution of the input data, and more. Unlike previous approaches, we propose to exploit an auxiliary dataset that is fully labeled to augment the quality of the learned representations. We also define the problem as cross domain clustering (XDC), which leverages adversarial learning to train an adaptive clustering model across text domains. Specifically, XDC jointly exploits a labeled source domain and an unlabeled target domain during model training. Owing to domain adversarial learning, the distribution shift across source and target domains could be mitigated. Moreover, XDC is implemented as a linkage-based clustering approach using graphs, which is agnostic of the number of clusters. We evaluate our XDC framework on three text datasets, and results show that it outperforms the state-of-the-art text clustering methods in most cases. Ablation studies and qualitative analysis also demonstrate the effectiveness of our framework.

Unsupervised domain adaptation (UDA) has become an appealing approach for knowledge transfer from a labeled source domain to an unlabeled target domain. However, when the classes in source and target domains are imbalanced, most existing UDA methods experience significant performance drop, as the decision boundary usually favors the majority classes. Some recent class-imbalanced domain adaptation (CDA) methods aim to tackle the challenge of biased label distribution by exploiting pseudo-labeled target samples during training process. However, these methods suffer from the issues with unreliable pseudo labels and error accumulation during training. In this paper, we propose a pairwise adversarial training approach for class-imbalanced domain adaptation. Unlike conventional adversarial training in which the adversarial samples are obtained from the lp ball of the original samples, we generate adversarial samples from the interpolated line of the aligned pairwise samples from source and target domains. The pairwise adversarial training (PAT) is a novel data-augmentation method which can be integrated into existing UDA models to tackle with the CDA problem. Experimental results and ablation studies show that the UDA models integrated with our method achieve considerable improvements on benchmarks compared with the original models as well as the state-of-the-art CDA methods. Our source code is available at: https://github.com/DamoSWL/Pairwise-Adversarial-Training

Unsupervised domain adaptation aims to learn domain-invariant features across domains to transfer knowledge from a well-labeled source domain to an unlabeled target domain. Recently, some unsupervised domain adaptation methods focus on semantically aligning data distributions with pseudo-labels of the target domain. However, semantic alignment based on pseudo-labels has potential risks, e.g., inaccurate pseudo-labeling from classifier, and error accumulation from pseudo-label bias. To alleviate these risks, we propose a novel self-supervision based semantic alignment (S3A) approach for unsupervised domain adaptation, which can jointly incorporate the source alignment and cross-domain target alignment for better semantic alignment across domains. S3A consists of a two-stage semantic alignment procedure with self-supervision. One is to capture the discriminative structure of source domain by aligning source data to source class prototypes, and the other is to match each target data to its neighbor in source domain with self-supervision. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our proposed method, compared with the representative adversarial learning and self-supervised learning based unsupervised domain adaptation methods.

Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain where both domains share a common label space and hold a private label space respectively. One of the most challenging goals in UniDA is to separate target samples from common classes and these from private classes without any prior knowledge on the target label space. In this paper, we propose a novel self-supervised adaptive memory network with consistency regularization for UniDA. The adaptive memory includes all target samples and source class centers, which dynamically divides target samples into common area, uncertain area, and unknown area based on the entropy. Our proposed framework jointly assigns a specific neighborhood to each target sample and clusters the target sample to its neighbor from the neighborhood. Most importantly, the proposed framework adopts consistency regularization that gradually makes the output of the classifier more reliable. This simple strategy is proved to be very effective for UniDA problem. Experimental results on two UniDA benchmarks demonstrate the effectiveness of our method.

Owing to the remarkable capability of extracting effective graph embeddings, graph convolutional network (GCN) and its variants have been successfully applied to a broad range of tasks, such as node classification, link prediction, and graph classification. Traditional GCN models suffer from the issues of overfitting and oversmoothing, while some recent techniques like DropEdge could alleviate these issues and thus enable the development of deep GCN. However, training GCN models is non-trivial, as it is sensitive to the choice of hyperparameters such as dropout rate and learning weight decay, especially for deep GCN models. In this paper, we aim to automate the training of GCN models through hyperparameter optimization. To be specific, we propose a self-tuning GCN approach with an alternate training algorithm, and further extend our approach by incorporating the population based training scheme. Experimental results on three benchmark datasets demonstrate the effectiveness of our approaches on optimizing multi-layer GCN, compared with several representative baselines.