A new upper bound is derived for the worst-case effect of selection bias on medical prediction model performance under partial observation of the selection process and target data.
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M³ constructs multi-scale variation-aware training measures via Morton octrees, cutting physics-weighted errors up to 4.7× and beating denser random data under aggressive subsampling.
The paper introduces two general frameworks for conditional two-sample testing by converting conditional independence tests or using density ratio estimation to enable marginal comparisons.
CS-ARM-BN uses negative control samples to stabilize Batch Normalization statistics in a meta-learning framework, achieving robust MoA classification on new experimental batches under label shift and small sample sizes.
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A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models
A new upper bound is derived for the worst-case effect of selection bias on medical prediction model performance under partial observation of the selection process and target data.
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M$^3$: Reframing Training Measures for Discretized Physical Simulations
M³ constructs multi-scale variation-aware training measures via Morton octrees, cutting physics-weighted errors up to 4.7× and beating denser random data under aggressive subsampling.
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General Frameworks for Conditional Two-Sample Testing
The paper introduces two general frameworks for conditional two-sample testing by converting conditional independence tests or using density ratio estimation to enable marginal comparisons.
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Stabilizing In-Context Multi-Source Domain Adaptation for Biomedical Images Through Controls
CS-ARM-BN uses negative control samples to stabilize Batch Normalization statistics in a meta-learning framework, achieving robust MoA classification on new experimental batches under label shift and small sample sizes.