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.
Angelopoulos, Stephen Bates, Clara Fannjiang, Michael I
6 Pith papers cite this work. Polarity classification is still indexing.
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NaiAD is a new dataset and framework for LLM-native advertising that uses decoupled generation and calibrated scoring to identify four semantic strategies for balancing user and commercial utilities.
LLMs show a consistent performance drop on arithmetic, spatial, and temporal reasoning tasks when framed in multi-turn dialogues versus isolated settings, demonstrated by the new BOULDER benchmark across eight travel-related tasks.
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
PPI++ yields easy-to-compute confidence sets for any-dimensional parameters that always improve on classical intervals from labeled data alone by leveraging abundant ML predictions.
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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NaiAD: Initiate Data-Driven Research for LLM Advertising
NaiAD is a new dataset and framework for LLM-native advertising that uses decoupled generation and calibrated scoring to identify four semantic strategies for balancing user and commercial utilities.
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Reasoning Gets Harder for LLMs Inside A Dialogue
LLMs show a consistent performance drop on arithmetic, spatial, and temporal reasoning tasks when framed in multi-turn dialogues versus isolated settings, demonstrated by the new BOULDER benchmark across eight travel-related tasks.
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Response Time Enhances Alignment with Heterogeneous Preferences
Response times modeled as drift-diffusion processes enable consistent estimation of population-average preferences from heterogeneous anonymous binary choices.
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PPI++: Efficient Prediction-Powered Inference
PPI++ yields easy-to-compute confidence sets for any-dimensional parameters that always improve on classical intervals from labeled data alone by leveraging abundant ML predictions.
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