StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
arXiv preprint arXiv:2505.13432 , year=
4 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 4verdicts
UNVERDICTED 4representative citing papers
LLM analysis of social media creates high-frequency CPI surrogates that, when jointly modeled in a deep panel framework, reduce short-term forecasting errors and better capture abrupt inflationary shifts than traditional econometric models.
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
Proposes framing auditing of deployed AI systems as continuous statistical monitoring of risk-controlled constraints like fairness and safety under uncertainty.
citing papers explorer
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Stable Localized Conformal Prediction via Transduction
StCP leverages transfer learning to stabilize the size of conformal prediction sets without additional target labels.
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How Does LLM Help Regional CPI Forecast: An LLM-powered Deep Panel Modeling Framework
LLM analysis of social media creates high-frequency CPI surrogates that, when jointly modeled in a deep panel framework, reduce short-term forecasting errors and better capture abrupt inflationary shifts than traditional econometric models.
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Estimate Level Adjustment For Inference With Proxies Under Random Distribution Shifts
A framework models proxy-primary outcome discrepancies as random effects at the parameter level, estimated from aggregated historical observations to calibrate inferences under distribution shifts.
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Towards Auditing AI Systems in the Wild
Proposes framing auditing of deployed AI systems as continuous statistical monitoring of risk-controlled constraints like fairness and safety under uncertainty.