77 citations · 213 across the 15 of their papers we have counts for
24 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…
CLEAR: Generative Counterfactual Explanations on Graphs
Jing Ma, Ruocheng Guo, Saumitra Mishra +2
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted lab…
Mitigating Popularity Bias in Recommendation with Unbalanced Interactions: A Gradient Perspective
Weijieying Ren, Lei Wang, Kunpeng Liu +3
Recommender systems learn from historical user-item interactions to identify preferred items for target users. These observed interactions are usually unbalanced following a long-t…
MLP4Rec: A Pure MLP Architecture for Sequential Recommendations
Muyang Li, Xiangyu Zhao, Chuan Lyu +3
Self-attention models have achieved state-of-the-art performance in sequential recommender systems by capturing the sequential dependencies among user-item interactions. However, t…
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…
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…