arXiv:2606. 15237v1 Announce Type: cross Abstract: Ensemble classifiers are predictive models that combine the results of simpler base models, often by majority vote.
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arXiv:2608.27704v1 Announce Type: new Abstract: When machine learning classifiers are retrained, inputs correctly classified by the previous model version may be misclassified by the updated version,...
arXiv:2607. 08522v1 Announce Type: new Abstract: The inherent rigidity of fixed-size benchmarks makes them an inefficient tool for model evaluation.
arXiv:2608. 13554v1 Announce Type: new Abstract: We study online probabilistic forecasting of binary outcomes chosen by an adaptive adversary.
arXiv:2503. 15581v2 Announce Type: replace Abstract: Real-time safety assessment is critical for ensuring the reliable operation of complex dynamic systems.
arXiv:2412. 12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confident and abstain when uncertainty is high.
arXiv:2607. 01417v1 Announce Type: new Abstract: Conditional inference trees (CIT) and conditional inference forests (CIF) reduce split-selection bias by testing features before choosing split thresholds, but repeated permutation tests and threshold searches can make these methods computationally expensive.