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.
Optimizing search engines using clickthrough data
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A neural sparse retrieval system with granular subword tokenization (max 3 chars) achieves 91.4% recall@10 on a 6M music document corpus versus 57.7% for trigrams, with improved HCI exploration efficiency and zero added query latency.
A pairwise-margin theory of ranking proves unique factor decompositions in the linear case, an interaction-curvature condition for nonlinear cases, and geometric structures including a competition-graph Laplacian and finite energy identities.
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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.
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Surface-Form Neural Sparse Retrieval: Robust Fuzzy Matching for Industrial Music Search
A neural sparse retrieval system with granular subword tokenization (max 3 chars) achieves 91.4% recall@10 on a 6M music document corpus versus 57.7% for trigrams, with improved HCI exploration efficiency and zero added query latency.
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A Mathematical Theory of Ranking
A pairwise-margin theory of ranking proves unique factor decompositions in the linear case, an interaction-curvature condition for nonlinear cases, and geometric structures including a competition-graph Laplacian and finite energy identities.