792 citations · 3.1k across the 112 of their papers we have counts for
13 papers · 1 filter
Graph Neural Networks with Diverse Spectral Filtering
Jingwei Guo, Kaizhu Huang, Xinping Yi +1
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph machine learning, with polynomial filters applied for graph convolutions, where all nodes share the…
Equitable Multi-task Learning
Jun Yuan, Rui Zhang
Multi-task learning (MTL) has achieved great success in various research domains, such as CV, NLP and IR etc. Due to the complex and competing task correlation, naive training all…
A method for incremental discovery of financial event types based on anomaly detection
Dianyue Gu, Zixu Li, Zhenhai Guan +2
Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the sa…
Adaptive Low-Precision Training for Embeddings in Click-Through Rate Prediction
Shiwei Li, Huifeng Guo, Lu Hou +5
Embedding tables are usually huge in click-through rate (CTR) prediction models. To train and deploy the CTR models efficiently and economically, it is necessary to compress their…
EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables
Penglei Gao, Xi Yang, Rui Zhang +3
While exogenous variables have a major impact on performance improvement in time series analysis, inter-series correlation and time dependence among them are rarely considered in t…
Regularization Penalty Optimization for Addressing Data Quality Variance in OoD Algorithms
Runpeng Yu, Hong Zhu, Kaican Li +5
Due to the poor generalization performance of traditional empirical risk minimization (ERM) in the case of distributional shift, Out-of-Distribution (OoD) generalization algorithms…
ES-GNN: Generalizing Graph Neural Networks Beyond Homophily with Edge Splitting
Jingwei Guo, Kaizhu Huang, Rui Zhang +1
While Graph Neural Networks (GNNs) have achieved enormous success in multiple graph analytical tasks, modern variants mostly rely on the strong inductive bias of homophily. However…
Multi View Spatial-Temporal Model for Travel Time Estimation
ZiChuan Liu, Zhaoyang Wu, Meng Wang +1
Taxi arrival time prediction is essential for building intelligent transportation systems. Traditional prediction methods mainly rely on extracting features from traffic maps, whic…
Memorize, Factorize, or be Naïve: Learning Optimal Feature Interaction Methods for CTR Prediction
Fuyuan Lyu, Xing Tang, Huifeng Guo +4
Click-through rate prediction is one of the core tasks in commercial recommender systems. It aims to predict the probability of a user clicking a particular item given user and ite…
Improving Model Robustness with Latent Distribution Locally and Globally
Zhuang Qian, Shufei Zhang, Kaizhu Huang +3
In this work, we consider model robustness of deep neural networks against adversarial attacks from a global manifold perspective. Leveraging both the local and global latent infor…
A Hybrid Approach to Privacy-Preserving Federated Learning
Stacey Truex, Nathalie Baracaldo, Ali Anwar +4
Federated learning facilitates the collaborative training of models without the sharing of raw data. However, recent attacks demonstrate that simply maintaining data locality durin…
Efficient Non-parametric Bayesian Hawkes Processes
Rui Zhang, Christian Walder, Marian-Andrei Rizoiu +1
In this paper, we develop an efficient nonparametric Bayesian estimation of the kernel function of Hawkes processes. The non-parametric Bayesian approach is important because it pr…
Matrix completion with deterministic pattern - a geometric perspective
Alexander Shapiro, Yao Xie, Rui Zhang
We consider the matrix completion problem with a deterministic pattern of observed entries. In this setting, we aim to answer the question: under what condition there will be (at l…