Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.
Information Processing and Management45(4), 427–437 (2009)
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MMM-Bench supplies 5,990 multi-modal documents from 12 commercial domains annotated along a 5-level taxonomy to test document classification under realistic business conditions.
EnCoDe enables design-time prediction of block-level energy consumption in Python code via static features and ML models trained on a dataset from 18,000 programs, achieving R²=0.75 and 80.6% hotspot classification accuracy.
A neurosymbolic pipeline extracts predicates from offer texts with an LLM and validates them via Logic Tensor Networks, delivering performance comparable to standard models plus built-in interpretability on a real corpus.
Coarsening smart meter load profile granularity produces two performance plateaus in socio-demographic inference (15 min–1 h and 1–7 days), enabling data minimization strategies that preserve some predictive utility.
Cascaded neural networks classify 10 eye-movement classes from single-cycle EOG signals at 99% accuracy with sub-83 ms latency below human reaction time.
EfficientNetB0 achieves the highest accuracy (95%) among five CNNs tested on multi-class brain tumor MRI classification, with notably better meningioma recall than shallower or custom models.
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Pandora's Regret: A Proper Scoring Rule for Evaluating Sequential Search
Pandora's Regret is a closed-form pairwise scoring rule derived from expected optimal search costs that elicits true probabilities and outperforms log loss, accuracy, and F1 at predicting diagnostic costs on MedMNIST models.