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Calibration-Aware Uncertainty Cascades is proposed, a simple post-hoc framework that independently calibrates each model's confidence and selects deployment policies using validation data and shows theoretically that calibration gives confidence thresholds an explicit selective-risk interpretation, whereas uncalibrated scores offer no comparable reliability guarantee.
AdaMuS overcomes the bias towards high-dimensional data in multi-view learning by adaptively pruning redundant parameters and sparsely fusing views, leading to improved performance on dimensionally unbalanced data.
