718 citations · 1.5k across the 30 of their papers we have counts for
31 papers
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Personalized decision-making can be implemented in a Federated learning (FL) framework that can collaboratively train a decision model by extracting knowledge across intelligent cl…
Task Aligned Generative Meta-learning for Zero-shot Learning
Zhe Liu, Yun Li, Lina Yao +2
Zero-shot learning (ZSL) refers to the problem of learning to classify instances from the novel classes (unseen) that are absent in the training set (seen). Most ZSL methods infer…
Generative Inverse Deep Reinforcement Learning for Online Recommendation
Xiaocong Chen, Lina Yao, Aixin Sun +3
Deep reinforcement learning enables an agent to capture user's interest through interactions with the environment dynamically. It has attracted great interest in the recommendation…
Face to Purchase: Predicting Consumer Choices with Structured Facial and Behavioral Traits Embedding
Zhe Liu, Xianzhi Wang, Lina Yao +3
Predicting consumers' purchasing behaviors is critical for targeted advertisement and sales promotion in e-commerce. Human faces are an invaluable source of information for gaining…
Spectrum-Guided Adversarial Disparity Learning
Zhe Liu, Lina Yao, Lei Bai +2
It has been a significant challenge to portray intraclass disparity precisely in the area of activity recognition, as it requires a robust representation of the correlation between…
Recommender Systems for the Internet of Things: A Survey
May Altulyan, Lina Yao, Xianzhi Wang +3
Recommendation represents a vital stage in developing and promoting the benefits of the Internet of Things (IoT). Traditional recommender systems fail to exploit ever-growing, dyna…
Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media
Hamad Zogan, Imran Razzak, Xianzhi Wang +2
Model interpretability has become important to engenders appropriate user trust by providing the insight into the model prediction. However, most of the existing machine learning m…
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting
Lei Bai, Lina Yao, Can Li +2
Modeling complex spatial and temporal correlations in the correlated time series data is indispensable for understanding the traffic dynamics and predicting the future status of an…
Causality Learning: A New Perspective for Interpretable Machine Learning
Guandong Xu, Tri Dung Duong, Qian Li +2
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation…
Agglomerative Neural Networks for Multi-view Clustering
Zhe Liu, Yun Li, Lina Yao +2
Conventional multi-view clustering methods seek for a view consensus through minimizing the pairwise discrepancy between the consensus and subviews. However, the pairwise compariso…
Multi-Center Federated Learning: Clients Clustering for Better Personalization
Guodong Long, Ming Xie, Tao Shen +4
Federated learning has received great attention for its capability to train a large-scale model in a decentralized manner without needing to access user data directly. It helps pro…
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
Zhe Liu, Lina Yao, Xianzhi Wang +2
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models. Given the effectiveness of generative adversarial learn…
Knowledge-guided Deep Reinforcement Learning for Interactive Recommendation
Xiaocong Chen, Chaoran Huang, Lina Yao +3
Interactive recommendation aims to learn from dynamic interactions between items and users to achieve responsiveness and accuracy. Reinforcement learning is inherently advantageous…
Survey for Trust-aware Recommender Systems: A Deep Learning Perspective
Manqing Dong, Feng Yuan, Lina Yao +3
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation r…
STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting
Lei Bai, Lina Yao, Salil. S Kanhere +2
Multi-step passenger demand forecasting is a crucial task in on-demand vehicle sharing services. However, predicting passenger demand over multiple time horizons is generally chall…
A Survey on Deep Learning-based Non-Invasive Brain Signals:Recent Advances and New Frontiers
Xiang Zhang, Lina Yao, Xianzhi Wang +3
Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices.…
Automatic Device Classification from Network Traffic Streams of Internet of Things
Lei Bai, Lina Yao, Salil S. Kanhere +2
With the widespread adoption of Internet of Things (IoT), billions of everyday objects are being connected to the Internet. Effective management of these devices to support reliabl…
Software Expert Discovery via Knowledge Domain Embeddings in a Collaborative Network
Chaoran Huang, Lina Yao, Xianzhi Wang +2
Community Question Answering (CQA) websites can be claimed as the most major venues for knowledge sharing, and the most effective way of exchanging knowledge at present. Considerin…
Expert Recommendation via Tensor Factorization with Regularizing Hierarchical Topical Relationships
Chaoran Huang, Lina Yao, Xianzhi Wang +3
Knowledge acquisition and exchange are generally crucial yet costly for both businesses and individuals, especially when the knowledge concerns various areas. Question Answering Co…
A Survey on Expert Recommendation in Community Question Answering
Xianzhi Wang, Chaoran Huang, Lina Yao +2
Community question answering (CQA) represents the type of Web applications where people can exchange knowledge via asking and answering questions. One significant challenge of most…
GrCAN: Gradient Boost Convolutional Autoencoder with Neural Decision Forest
Manqing Dong, Lina Yao, Xianzhi Wang +2
Random forest and deep neural network are two schools of effective classification methods in machine learning. While the random forest is robust irrespective of the data domain, th…
Interpretable Parallel Recurrent Neural Networks with Convolutional Attentions for Multi-Modality Activity Modeling
Kaixuan Chen, Lina Yao, Xianzhi Wang +4
Multimodal features play a key role in wearable sensor-based human activity recognition (HAR). Selecting the most salient features adaptively is a promising way to maximize the eff…
Opinion Fraud Detection via Neural Autoencoder Decision Forest
Manqing Dong, Lina Yao, Xianzhi Wang +3
Online reviews play an important role in influencing buyers' daily purchase decisions. However, fake and meaningless reviews, which cannot reflect users' genuine purchase experienc…
Know Your Mind: Adaptive Brain Signal Classification with Reinforced Attentive Convolutional Neural Networks
Xiang Zhang, Lina Yao, Xianzhi Wang +3
Electroencephalography (EEG) signals reflect activities on certain brain areas. Effective classification of time-varying EEG signals is still challenging. First, EEG signal process…