1.5k citations · 5.2k across the 61 of their papers we have counts for
16 papers · 1 filter
Global Convolutional Neural Processes
Xuesong Wang, Lina Yao, Xianzhi Wang +2
The ability to deal with uncertainty in machine learning models has become equally, if not more, crucial to their predictive ability itself. For instance, during the pandemic, gove…
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference
Xiaocong Chen, Lina Yao, Xianzhi Wang +3
Recent advances in reinforcement learning have inspired increasing interest in learning user modeling adaptively through dynamic interactions, e.g., in reinforcement learning based…
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…
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…
NP-PROV: Neural Processes with Position-Relevant-Only Variances
Xuesong Wang, Lina Yao, Xianzhi Wang +1
Neural Processes (NPs) families encode distributions over functions to a latent representation, given context data, and decode posterior mean and variance at unknown locations. Sin…
Adversarial Attacks and Detection on Reinforcement Learning-Based Interactive Recommender Systems
Yuanjiang Cao, Xiaocong Chen, Lina Yao +2
Adversarial attacks pose significant challenges for detecting adversarial attacks at an early stage. We propose attack-agnostic detection on reinforcement learning-based interactiv…
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…
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…
Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph
Lu Liu, Tianyi Zhou, Guodong Long +3
A variety of machine learning applications expect to achieve rapid learning from a limited number of labeled data. However, the success of most current models is the result of heav…
DARec: Deep Domain Adaptation for Cross-Domain Recommendation via Transferring Rating Patterns
Feng Yuan, Lina Yao, Boualem Benatallah
Cross-domain recommendation has long been one of the major topics in recommender systems. Recently, various deep models have been proposed to transfer the learned knowledge across…
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…
Adversarial Variational Embedding for Robust Semi-supervised Learning
Xiang Zhang, Lina Yao, Feng Yuan
Semi-supervised learning is sought for leveraging the unlabelled data when labelled data is difficult or expensive to acquire. Deep generative models (e.g., Variational Autoencoder…
Quaternion Knowledge Graph Embeddings
Shuai Zhang, Yi Tay, Lina Yao +1
In this work, we move beyond the traditional complex-valued representations, introducing more expressive hypercomplex representations to model entities and relations for knowledge…
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…
Adversarially Regularized Graph Autoencoder for Graph Embedding
Shirui Pan, Ruiqi Hu, Guodong Long +3
Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the…
DeepKey: An EEG and Gait Based Dual-Authentication System
Xiang Zhang, Lina Yao, Chaoran Huang +3
Biometric authentication involves various technologies to identify individuals by exploiting their unique, measurable physiological and behavioral characteristics. However, traditi…