A framework proves that broad recalibrated leakage is undetectable from predictions alone without an external discrimination ceiling, while near-label leaks produce a detectable unit-purity signature yielding a prior-free test.
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11 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
A PC-based decomposition of FVE into low- and high-dimensional components reduces bias when applying GWASH or LMM-REML to strongly correlated high-dimensional predictors.
AtlasGS uses shared subject-specific Gaussian geometry learned from isotropic scans to achieve through-plane super-resolution and multi-modal harmonization in brain MRI with reported state-of-the-art fidelity on UK Biobank, GBM, and ABCD datasets.
Wearable accelerometry, EDA, and temperature data from 9 students with profound autism, processed with fine-tuned foundation models, enables prediction of challenging behavior episodes up to 10 minutes in advance at AUC-ROC 0.78 in actual classroom sessions.
BTECF encodes retinal vessels as Bézier trees to enable targeted, parameter-level counterfactual interventions on vessel geometry for causal analysis of vascular diseases.
REVEAL uses vision-language alignment of retinal morphometry and clinical risk narratives plus group contrastive learning to predict AD and dementia about 8 years early.
Complex multimodal architectures do not reliably outperform unimodal baselines or a simple multimodal baseline under standardized evaluation.
Gradient boosting produces risk scores with competitive accuracy but 60% fewer rules on classification tasks and 16% fewer on time-to-event tasks than regression-based methods like AutoScore.
Introduces a calibration framework for AI benchmarks using world-population probability levels on logarithmic scales derived from human test data and LLM extrapolation.
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.
citing papers explorer
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A prior-free blind detection of information leakage from model predictions
A framework proves that broad recalibrated leakage is undetectable from predictions alone without an external discrimination ceiling, while near-label leaks produce a detectable unit-purity signature yielding a prior-free test.
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Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
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Principal Components Decomposition of Fraction of Variance Explained in High Dimensional Linear Models with Strong Correlation
A PC-based decomposition of FVE into low- and high-dimensional components reduces bias when applying GWASH or LMM-REML to strongly correlated high-dimensional predictors.
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AtlasGS: Brain MRI Spatial Resolution Harmonization With Shared Gaussian Geometry
AtlasGS uses shared subject-specific Gaussian geometry learned from isotropic scans to achieve through-plane super-resolution and multi-modal harmonization in brain MRI with reported state-of-the-art fidelity on UK Biobank, GBM, and ABCD datasets.
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Prediction of Challenging Behaviors Associated with Profound Autism in a Classroom Setting Using Wearable Sensors
Wearable accelerometry, EDA, and temperature data from 9 students with profound autism, processed with fine-tuned foundation models, enables prediction of challenging behavior episodes up to 10 minutes in advance at AUC-ROC 0.78 in actual classroom sessions.
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A General B\'ezier Tree Encoding Counterfactual Framework for Retinal-Vessel-Mediated Disease Analysis
BTECF encodes retinal vessels as Bézier trees to enable targeted, parameter-level counterfactual interventions on vessel geometry for causal analysis of vascular diseases.
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REVEAL: Multimodal Vision-Language Alignment of Retinal Morphometry and Clinical Risks for Incident AD and Dementia Prediction
REVEAL uses vision-language alignment of retinal morphometry and clinical risk narratives plus group contrastive learning to predict AD and dementia about 8 years early.
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Fusion or Confusion? Multimodal Complexity Is Not All You Need
Complex multimodal architectures do not reliably outperform unimodal baselines or a simple multimodal baseline under standardized evaluation.
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Gradient Boosted Risk Scores
Gradient boosting produces risk scores with competitive accuracy but 60% fewer rules on classification tasks and 16% fewer on time-to-event tasks than regression-based methods like AutoScore.
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From Human-Level AI Tales to AI Leveling Human Scales
Introduces a calibration framework for AI benchmarks using world-population probability levels on logarithmic scales derived from human test data and LLM extrapolation.
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Modality vs. Morphology: A Framework for Time Series Classification for Biological Signals
A review synthesizes evidence from EEG, EMG, ECG, PPG and ocular signals to argue that waveform morphology, rather than modality or model class, primarily determines TSC performance and interpretability.