Nowadays, many network representation learning algorithms and downstream network mining tasks have already paid attention to dynamic networks or temporal networks, which are more suitable for real-world complex scenarios by modeling evolving patterns and temporal dependencies between node interactions.
In this paper, we study a novel problem of multi-facet bandits involving a group of bandits, each characterizing the users' needs from one unique aspect.
A better way is to present a sequence of products with increasingly floral attributes based on the white dress, and allow the customer to select the most satisfactory one from the sequence.
For example, given the diagnostic result that a model provided based on the X-ray images of a patient at different poses, the doctor needs to know why the model made such a prediction.
One major challenge associated with continuous transfer learning is the time evolving relatedness of the source domain and the current target domain as the target domain evolves over time.
Recommender systems are popular tools for information retrieval tasks on a large variety of web applications and personalized products.
To preserve the privacy of the clients, modern decentralized learning paradigms require each client to maintain a private local training data set and only upload their summarized model updates to the server.
Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.
In this paper, we study the problem of outlier arm detection in multi-armed bandit settings, which finds plenty of applications in many high-impact domains such as finance, healthcare, and online advertising.
Graph data widely exist in many high-impact applications.
Ranked #1 on Node Classification on Facebook
However, there is no existing deep learning algorithm that jointly models task and view dual heterogeneity, particularly for a data set with multiple modalities (text and image mixed data set or text and video mixed data set, etc.).
The unprecedented demand for large amount of data has catalyzed the trend of combining human insights with machine learning techniques, which facilitate the use of crowdsourcing to enlist label information both effectively and efficiently.
Then, based on VDRW, we propose a semi-supervised network representation learning framework named ImVerde for imbalanced networks, in which context sampling uses VDRW and the label information to create node-context pairs, and balanced-batch sampling adopts a simple under-sampling method to balance these pairs in different classes.
Social and Information Networks
With the increasing demand for large amount of labeled data, crowdsourcing has been used in many large-scale data mining applications.
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.