Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
Review of Economics and Statistics, 90(3):414–427
4 Pith papers cite this work. Polarity classification is still indexing.
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Proposes a minimum measurement standard for LLM-as-a-judge in multi-hop RAG that fixes budgets and requires cluster-aware inference, showing it alters which baseline comparisons remain significant.
Simulations show two-party systems moderate policy positions while multiparty systems increase polarization, with turnout and activists further driving extremes.
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.
citing papers explorer
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The Statistical Cost of Adaptation in Multi-Source Transfer Learning
Multi-source transfer learning incurs an intrinsic adaptation cost that can exceed one, with phase transitions separating regimes where bias-agnostic estimators match oracle performance from those where they cannot.
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A Fixed-Budget, Cluster-Aware Standard for LLM-as-a-Judge Evaluation: A Multi-Hop RAG Stress Test
Proposes a minimum measurement standard for LLM-as-a-judge in multi-hop RAG that fixes budgets and requires cluster-aware inference, showing it alters which baseline comparisons remain significant.
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A computational model of spatial politics: Hotelling-Downs model as statistical physics
Simulations show two-party systems moderate policy positions while multiparty systems increase polarization, with turnout and activists further driving extremes.
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Factor multivariate stochastic volatility models of high dimension
Proposes fMSV framework using factor decomposition, two-stage estimation, and derived asymptotics for high-dimensional multivariate stochastic volatility, tested via simulations and portfolio applications.