601 citations · 2k across the 47 of their papers we have counts for
6 papers · 1 filter
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…
Privacy-Aware Recommendation with Private-Attribute Protection using Adversarial Learning
Ghazaleh Beigi, Ahmadreza Mosallanezhad, Ruocheng Guo +3
Recommendation is one of the critical applications that helps users find information relevant to their interests. However, a malicious attacker can infer users' private information…
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.…
Detecting Fake News with Weak Social Supervision
Kai Shu, Ahmed Hassan Awadallah, Susan Dumais +1
Limited labeled data is becoming the largest bottleneck for supervised learning systems. This is especially the case for many real-world tasks where large scale annotated examples…
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…
I Am Not What I Write: Privacy Preserving Text Representation Learning
Ghazaleh Beigi, Kai Shu, Ruocheng Guo +2
Online users generate tremendous amounts of textual information by participating in different activities, such as writing reviews and sharing tweets. This textual data provides opp…