MOSAIC learns overlap-aware shared-specific representations, fits a first-stage predictor on overlapping data, and calibrates the gap using target-pattern samples, with non-asymptotic error bounds decomposing overlap size, calibration gap, and representation error.
URL https://www.sciencedirect.com/science/ article/pii/S1574013724001035
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This review synthesizes imputation methods from classical statistics to deep learning and LLMs, examines their integration with downstream tasks, and outlines challenges for future work.
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Pattern-Calibrated Multimodal Prediction under Blockwise Missingness
MOSAIC learns overlap-aware shared-specific representations, fits a first-stage predictor on overlapping data, and calibrates the gap using target-pattern samples, with non-asymptotic error bounds decomposing overlap size, calibration gap, and representation error.