Introduces budgeted heteroskedastic multi-judge estimation and proves instance-optimality of an adaptive inverse-variance weighted estimator via matching upper and lower bounds.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
A Minkowski-type Wasserstein framework for location-scale mixtures reduces multimarginal OT to discrete component transport with linear complexity and shows competitive domain adaptation performance.
For 2-layer homogeneous NNs on multi-index models, flattest interpolators achieve low population loss when data is a sum of single-index models with low approximation error and label noise.
citing papers explorer
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Instance-Optimal Estimation with Multiple LLM Judges on a Budget
Introduces budgeted heteroskedastic multi-judge estimation and proves instance-optimality of an adaptive inverse-variance weighted estimator via matching upper and lower bounds.
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Minkowski-Type Wasserstein Metrics and Barycenters for Location-Scale Mixtures with Application to Domain Adaptation
A Minkowski-type Wasserstein framework for location-scale mixtures reduces multimarginal OT to discrete component transport with linear complexity and shows competitive domain adaptation performance.
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Flatness and Generalization: Learning Multi-Index Models with Homogeneous Neural Networks
For 2-layer homogeneous NNs on multi-index models, flattest interpolators achieve low population loss when data is a sum of single-index models with low approximation error and label noise.