6 papers
Consistency evaluation of benchmarks used for causal discovery
Yuzhe Zhang, Chihui Chen, Lina Yao +1
In graphical causal model, causal discovery aims to construct a causal graph based on numerical data and domain knowledge in plain text. However, the evaluation of causal discovery…
Learning Latent Dynamical Causal Processes for Single-Cell Perturbation Prediction
Wenkang Jiang, Yuhang Liu, Erdun Gao +3
Single-cell perturbation prediction aims to infer how cells respond to unseen interventions and to achieve out-of-distribution (OOD) generalization, providing a computational route…
What Makes a Representation Good for Single-Cell Perturbation Prediction?
Wenkang Jiang, Yuhang Liu, Yichao Cai +5
Single-cell perturbation modeling is fundamental for understanding and predicting cellular responses to genetic perturbations. However, existing approaches, from causal representat…
Retrieval-Augmented Review Generation for Poisoning Recommender Systems
Shiyi Yang, Xinshu Li, Guanglin Zhou +4
Recent studies have shown that recommender systems (RSs) are highly vulnerable to data poisoning attacks, where malicious actors inject fake user profiles, including a group of wel…
Causality-aligned Prompt Learning via Diffusion-based Counterfactual Generation
Xinshu Li, Ruoyu Wang, Erdun Gao +2
Prompt learning has garnered attention for its efficiency over traditional model training and fine-tuning. However, existing methods, constrained by inadequate theoretical foundati…
Self-Distilled Disentangled Learning for Counterfactual Prediction
Xinshu Li, Mingming Gong, Lina Yao
The advancements in disentangled representation learning significantly enhance the accuracy of counterfactual predictions by granting precise control over instrumental variables, c…