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Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCN) can effectively learn node representations via aggregating neighbors based on the relation graph. However, despite a few exceptions, most of the previous work in this line does not ...

As a significant extension of classical clustering methods, ensemble clustering first generates multiple basic clusterings and then fuses them into one consensus partition by solving a problem concerning graph partition with respect to the co-association ...

Given a million escort advertisements, how can we spot near-duplicates? Such micro-clusters of ads are usually signals of human trafficking (HT). How can we summarize them to convince law enforcement to act? Spotting micro-clusters of near-duplicate ...

Weakly Supervised Semantic Segmentation with image-level annotation uses localization maps from the classifier to generate pseudo labels. However, such localization maps focus only on sparse salient object regions, it is difficult to generate high-quality ...

Personalized federated learning (PFL) has emerged as a paradigm to provide a personalized model that can fit the local data distribution of each client. One natural choice for PFL is to leverage the fast adaptation capability of meta-learning, where it ...

Machine learning models are becoming pervasive in high-stakes applications. Despite their clear benefits in terms of performance, the models could show discrimination against minority groups and result in fairness issues in a decision-making process, ...

Low-rank approximation models of data matrices have become important machine learning and data mining tools in many fields, including computer vision, text mining, bioinformatics, and many others. They allow for embedding high-dimensional data into low-...

With the rapidly growing attention to multi-view data in recent years, multi-view outlier detection has become a rising field with intense research. These researches have made some success, but still exist some issues that need to be solved. First, many ...

Learning from streaming data is challenging as the distribution of incoming data may change over time, a phenomenon known as concept drift. The predictive patterns, or experience learned under one distribution may become irrelevant as conditions change ...

In this study, sentiment classification and emotion distribution learning across domains are both formulated as a semi-supervised domain adaptation problem, which utilizes a small amount of labeled documents in the target domain for model training. By ...

With the rapid development of data stream, multi-label algorithms for mining dynamic data become more and more important. At the same time, when data distribution changes, concept drift will occur, which will make the existing classification models lose ...

Given a dense tensor, how can we efficiently discover hidden relations and patterns in static and online streaming settings? Tucker decomposition is a fundamental tool to analyze multidimensional arrays in the form of tensors. However, existing Tucker ...

Crowdsourcing truth inference aims to assign a correct answer to each task from candidate answers that are provided by crowdsourced workers. A common approach is to generate workers’ reliabilities to represent the quality of answers. Although crowdsourced ...

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus ...

Transportation demand forecasting is a critical precondition of optimal online transportation dispatch, which will greatly reduce drivers’ wasted mileage and customers’ waiting time, contributing to economic and environmental sustainability. Though ...

Accurate citywide traffic inference is critical for improving intelligent transportation systems with smart city applications. However, this task is very challenging given the limited training data, due to the high cost of sensor installment and ...

In this article, we formulate lifelong learning as an online transfer learning procedure over consecutive tasks, where learning a given task depends on the accumulated knowledge. We propose a novel theoretical principled framework, lifelong online ...

What is the best way to match the nodes of two graphs? This graph alignment problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches ...

Despite substantial interest in applications of neural networks to information retrieval, neural ranking models have mostly been applied to conventional ad-hoc retrieval tasks over web pages and newswire articles. This article proposes a concept-enhanced ...

Time series classification has become an interesting field of research, thanks to the extensive studies conducted in the past two decades. Time series may have missing data, which may affect both the representation and also modeling of time series. Thus, ...