4 papers
GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation
Qianqian Wang, Yunshan Li, Jiawen Zeng +2
Serving useful recommendations under distribution shift is crucial for balancing utility and risk in out-of-distribution (OOD) recommendation. However, most existing OOD methods im…
ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning
Qianqian Wang, Yunshan Li, Dawei Huang +2
Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable. However, existing methods often infer environments from…
Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation
Qianqian Wang, Wenwu Gong, Yunshan Li +3
Out-of-distribution (OOD) recommendation is vulnerable to preference shifts induced by a latent environment. Existing methods can infer latent states from logged interactions, yet…
LRTuckerRep: Low-rank Tucker Representation Model for Multi-dimensional Data Completion
Wenwu Gong, Lili Yang
Multi-dimensional data completion is a critical problem in computational sciences, particularly in domains such as computer vision, signal processing, and scientific computing. Exi…