PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
arXiv preprint arXiv:2106.09848 , year=
5 Pith papers cite this work, alongside 5 external citations. Polarity classification is still indexing.
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ELCP integrates auxiliary data with a density-ratio-weighted kernel to enhance localized conformal prediction sets, maintaining marginal coverage and improving asymptotic local coverage.
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
PASC converts multi-stage joint coverage into a single scalar conformal problem on the joint max nonconformity score, delivering finite-sample distribution-free guarantees and higher empirical coverage than Bonferroni or independent calibration.
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.
citing papers explorer
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In-Context Positive-Unlabeled Learning
PUICL is a transformer pretrained on synthetic PU data from structural causal models that solves positive-unlabeled classification via in-context learning without gradient updates or fitting.
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Enhanced localized conformal prediction with imperfect auxiliary information
ELCP integrates auxiliary data with a density-ratio-weighted kernel to enhance localized conformal prediction sets, maintaining marginal coverage and improving asymptotic local coverage.
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Conditional Predictive Inference for General Structured Data with Group Symmetries
C-SymmPI reformulates conditional coverage as miscoverage error over a user-specified function class to deliver near-conditional guarantees under group symmetries and distributional invariance.
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PASC: Pipeline-Aware Conformal Prediction with Joint Coverage Guarantees for Multi-Stage NLP and LLM Pipelines
PASC converts multi-stage joint coverage into a single scalar conformal problem on the joint max nonconformity score, delivering finite-sample distribution-free guarantees and higher empirical coverage than Bonferroni or independent calibration.
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Weight Clipping for Robust Conformal Inference under Unbounded Covariate Shifts
Clipped least-squares importance fitting enables weighted conformal prediction to achieve dataset-conditional coverage guarantees under unbounded covariate shifts by bounding undercoverage and estimating a corrective inflation factor from data.