601 citations · 2k across the 47 of their papers we have counts for
15 papers · 1 filter
Estimating Causal Effects of Multi-Aspect Online Reviews with Multi-Modal Proxies
Lu Cheng, Ruocheng Guo, Huan Liu
Online reviews enable consumers to engage with companies and provide important feedback. Due to the complexity of the high-dimensional text, these reviews are often simplified as a…
Graph Few-shot Class-incremental Learning
Zhen Tan, Kaize Ding, Ruocheng Guo +1
The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems. A large portion of high-impact applications like social media, recommenda…
Meta Propagation Networks for Graph Few-shot Semi-supervised Learning
Kaize Ding, Jianling Wang, James Caverlee +1
Inspired by the extensive success of deep learning, graph neural networks (GNNs) have been proposed to learn expressive node representations and demonstrated promising performance…
Effects of Multi-Aspect Online Reviews with Unobserved Confounders: Estimation and Implication
Lu Cheng, Ruocheng Guo, Kasim Selcuk Candan +1
Online review systems are the primary means through which many businesses seek to build the brand and spread their messages. Prior research studying the effects of online reviews h…
Graph Learning: A Survey
Feng Xia, Ke Sun, Shuo Yu +4
Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social sys…
Few-shot Network Anomaly Detection via Cross-network Meta-learning
Kaize Ding, Qinghai Zhou, Hanghang Tong +1
Network anomaly detection aims to find network elements (e.g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority. It has a profound impact in a…
Causal Adversarial Network for Learning Conditional and Interventional Distributions
Raha Moraffah, Bahman Moraffah, Mansooreh Karami +2
We propose a generative Causal Adversarial Network (CAN) for learning and sampling from conditional and interventional distributions. In contrast to the existing CausalGAN which re…
Graph Prototypical Networks for Few-shot Learning on Attributed Networks
Kaize Ding, Jianling Wang, Jundong Li +3
Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central an…
Leveraging Multi-Source Weak Social Supervision for Early Detection of Fake News
Kai Shu, Guoqing Zheng, Yichuan Li +4
Social media has greatly enabled people to participate in online activities at an unprecedented rate. However, this unrestricted access also exacerbates the spread of misinformatio…
Causal Interpretability for Machine Learning -- Problems, Methods and Evaluation
Raha Moraffah, Mansooreh Karami, Ruocheng Guo +2
Machine learning models have had discernible achievements in a myriad of applications. However, most of these models are black-boxes, and it is obscure how the decisions are made b…
Counterfactual Evaluation of Treatment Assignment Functions with Networked Observational Data
Ruocheng Guo, Jundong Li, Huan Liu
Counterfactual evaluation of novel treatment assignment functions (e.g., advertising algorithms and recommender systems) is one of the most crucial causal inference problems for pr…
Deep causal representation learning for unsupervised domain adaptation
Raha Moraffah, Kai Shu, Adrienne Raglin +1
Studies show that the representations learned by deep neural networks can be transferred to similar prediction tasks in other domains for which we do not have enough labeled data.…
Feature Interaction-aware Graph Neural Networks
Kaize Ding, Yichuan Li, Jundong Li +2
Inspired by the immense success of deep learning, graph neural networks (GNNs) are widely used to learn powerful node representations and have demonstrated promising performance on…
Online Newton Step Algorithm with Estimated Gradient
Binbin Liu, Jundong Li, Yunquan Song +3
Online learning with limited information feedback (bandit) tries to solve the problem where an online learner receives partial feedback information from the environment in the cour…
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Guansong Pang, Longbing Cao, Ling Chen +1
Learning expressive low-dimensional representations of ultrahigh-dimensional data, e.g., data with thousands/millions of features, has been a major way to enable learning methods t…