arXiv Machine Learning
An Identifiability Theory of Masked Prediction: Mode Blindness and Mask Schedules
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv Machine Learning
Aug 4
On the Identifiability of Masked Prediction: Mode Blindness and Mask Schedules
arXiv:2608. 01383v1 Announce Type: new Abstract: Masked prediction learns representations by fitting a schedule-weighted family of conditional laws, but it remains unclear when near-optimal conditional prediction pins down the underlying joint law.
arXiv Machine Learning
Jun 11
A prior-free blind detection of information leakage from model predictions
arXiv:2606. 11267v1 Announce Type: new Abstract: Data leakage -- contamination of a model with information unavailable at baseline -- is the dominant reproducibility failure in machine-learning-based science, yet detection tools require training code, external data, or domain expertise.
arXiv AI
Aug 7
GAUGE: Granularity-Adaptive Counterfactual Gating of Evidence for Incomplete Multimodal Classification
arXiv:2608. 05608v1 Announce Type: cross Abstract: Multimodal classification typically assumes all modalities are available, yet real-world inputs are often incomplete.
arXiv Machine Learning
Aug 10
Conformal Fusion Under Missing Modalities
arXiv:2608. 07183v1 Announce Type: new Abstract: Multimodal fusion architectures typically assume all modalities are available at inference, yet sensor failures, acquisition variability, and cost constraints routinely produce incomplete observations.
arXiv Statistics ML
Aug 26
Learning from Uncertainty-dependent Missing Labels for Semi-supervised Classification
arXiv:2608.23960v1 Announce Type: cross Abstract: Missing labels are usually regarded as a source of information loss in classification. We study a semi-supervised setting in which the probability of...
arXiv Machine Learning
Jul 31
TIER-MoE: Trust-Informed Expert Routing via Conditional Modality Risk for Multimodal Fusion in Biomedical Classification
arXiv:2607. 27289v1 Announce Type: new Abstract: The promise of multimodal fusion lies in combining complementary sources of evidence, yet more evidence does not always yield a better prediction.