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cs.LG2026
Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency
Md Abdullah Al Forhad, Weishi Shi
While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors. This is because the data sampled for active lea…
cs.LG2026
Metric-Normalized Posterior Leakage (mPL): Attacker-Aligned Privacy for Joint Consumption
Gaoyi Chen, Minghao Li, Weishi Shi +4
Metric differential privacy (mDP) strengthens local differential privacy (LDP) by scaling noise to semantic distance, but many machine learning (ML) systems are consumed under join…
cs.LG2025
Unveiling Statistical Significance of Online Regression over Multiple Datasets
Mohammad Abu-Shaira, Weishi Shi
Despite extensive focus on techniques for evaluating the performance of two learning algorithms on a single dataset, the critical challenge of developing statistical tests to compa…