99 citations · 120 across the 7 of their papers we have counts for
7 papers
Distributional Shift Adaptation using Domain-Specific Features
Anique Tahir, Lu Cheng, Ruocheng Guo +1
Machine learning algorithms typically assume that the training and test samples come from the same distributions, i.e., in-distribution. However, in open-world scenarios, streaming…
Toward Understanding Bias Correlations for Mitigation in NLP
Lu Cheng, Suyu Ge, Huan Liu
Natural Language Processing (NLP) models have been found discriminative against groups of different social identities such as gender and race. With the negative consequences of the…
Causal Disentanglement with Network Information for Debiased Recommendations
Paras Sheth, Ruocheng Guo, Lu Cheng +2
Recommender systems aim to recommend new items to users by learning user and item representations. In practice, these representations are highly entangled as they consist of inform…
Deep Graph Learning for Anomalous Citation Detection
Jiaying Liu, Feng Xia, Xu Feng +2
Anomaly detection is one of the most active research areas in various critical domains, such as healthcare, fintech, and public security. However, little attention has been paid to…
Domain Adaptive Fake News Detection via Reinforcement Learning
Ahmadreza Mosallanezhad, Mansooreh Karami, Kai Shu +2
With social media being a major force in information consumption, accelerated propagation of fake news has presented new challenges for platforms to distinguish between legitimate…
Evaluation Methods and Measures for Causal Learning Algorithms
Lu Cheng, Ruocheng Guo, Raha Moraffah +3
The convenient access to copious multi-faceted data has encouraged machine learning researchers to reconsider correlation-based learning and embrace the opportunity of causality-ba…