Shuyuan Xu

Personalized Counterfactual Fairness in Recommendation

2021 · arXiv (Cornell University)

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness …

Towards Personalized Fairness based on Causal Notion

2021

Recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness …

Deconfounded Causal Collaborative Filtering

2021 · arXiv (Cornell University)

Recommender systems may be confounded by various types of confounding factors (also called confounders) that may lead to inaccurate recommendations and sacrificed recommendation performance. Current approaches to solving the problem usually design each specific model …

Statistical learning of stochastic complex systems via the Yau-Yau nonlinear filter

2026 · The Innovation

Complex systems are characterized by nonlinearity, uncertainty, and noise interference, making it challenging to unravel their internal operation mechanisms. Here, we overcome this issue by implementing the Yau-Yau nonlinear filter theory to reconstruct stochastic networks …