124 citations · 505 across the 32 of their papers we have counts for
7 papers · 1 filter
A Surrogate Objective Framework for Prediction+Optimization with Soft Constraints
Kai Yan, Jie Yan, Chuan Luo +3
Prediction+optimization is a common real-world paradigm where we have to predict problem parameters before solving the optimization problem. However, the criteria by which the pred…
A Unified and Fast Interpretable Model for Predictive Analytics
Yuanyuan Jiang, Rui Ding, Tianchi Qiao +3
Predictive analytics aims to build machine learning models to predict behavior patterns and use predictions to guide decision-making. Predictive analytics is human involved, thus t…
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily
Lun Du, Xiaozhou Shi, Qiang Fu +4
Graph Neural Networks (GNNs) are widely used on a variety of graph-based machine learning tasks. For node-level tasks, GNNs have strong power to model the homophily property of gra…
ML4C: Seeing Causality Through Latent Vicinity
Haoyue Dai, Rui Ding, Yuanyuan Jiang +2
Supervised Causal Learning (SCL) aims to learn causal relations from observational data by accessing previously seen datasets associated with ground truth causal relations. This pa…
Neuron Campaign for Initialization Guided by Information Bottleneck Theory
Haitao Mao, Xu Chen, Qiang Fu +3
Initialization plays a critical role in the training of deep neural networks (DNN). Existing initialization strategies mainly focus on stabilizing the training process to mitigate…
TabularNet: A Neural Network Architecture for Understanding Semantic Structures of Tabular Data
Lun Du, Fei Gao, Xu Chen +5
Tabular data are ubiquitous for the widespread applications of tables and hence have attracted the attention of researchers to extract underlying information. One of the critical p…
Understanding and Improvement of Adversarial Training for Network Embedding from an Optimization Perspective
Lun Du, Xu Chen, Fei Gao +4
Network Embedding aims to learn a function mapping the nodes to Euclidean space contribute to multiple learning analysis tasks on networks. However, the noisy information behind th…