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cs.LG2026
A Decision-Support Audit Protocol for Supervision Drift in Proxy-Labeled Credit-Risk Prediction
Mehrdad Shoeibi, Muhammad Shabanpour, Waldemar Karwowski +1
Credit-risk models are trained on proxy labels and deployed under temporal and segment change, yet no single transfer metric separates base-rate shift, probability-scale shift, and…
cs.LG2026
Can We Trust In-Distribution Success? Locked Evaluation Reveals Transfer Failure and Sampling-Depth Entanglement in CRISPRi Perturbation Prediction
Mehrdad Shoeibi, Niloofar Yousefi
AI evaluation can support the wrong inference when an in-domain benchmark success does not survive distribution shift, or when the benchmark endpoint is entangled with a design fac…
cs.LG2026
Learning Stable Predictors from Weak Supervision under Distribution Shift
Mehrdad Shoeibi, Elias Hossain, Ivan Garibay +1
Learning from weak, proxy, or relative supervision is common when ground-truth labels are unavailable, but robustness under distribution shift remains poorly understood because the…