5 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…
Spatiotemporal Regularized Tucker Decomposition Approach for Traffic Data Imputation
Wenwu Gong, Zhejun Huang, Lili Yang
In intelligent transportation systems, traffic data imputation, estimating the missing value from partially observed data is an inevitable and challenging task. Previous studies ha…