Author

Qianqian Wang

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Preprint Aug 2026

ProME: Prototype-Margin Environments with Repair-Aware Selection for Group-Robust Learning

Group-robust learning is crucial for maintaining accuracy on rare subpopulations when training-group labels are unavailable. However, existing methods often infer environments from a separate reference model and select representations before fitting the classifier used at deployment, leaving both decisions misaligned w...

Qianqian Wang, Yun-Shan Li, Dawei Huang et al. · 0 citations

Preprint Aug 2026

Conditionally Identifiable Latent-Environment Modeling for Out-of-Distribution Recommendation

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 the statistical meaning of the latent environment and its effect on preference remain underdetermined. We formulate this task as conditional...

Qianqian Wang, W. Gong, Yun-Shan Li et al. · 0 citations

#machine learning Preprint Sep 2026

GenCAR: Generative Counterfactual Alignment with Risk-Controlled Selection for Out-of-Distribution Recommendation

GenCAR is proposed, which couples preference-grounded counterfactual supervision with calibrated set selection with calibrated set selection, and theoretically bound conditional counterfactual approximation error and prove finite-sample, distribution-free control of proxy-label FDR under exchangeability and positive re...

Qianqian Wang, Yun-Shan Li, Jian Zeng et al. · 0 citations