PS-DME is a new framework that controls post-selection false coverage rate for distributional KPI estimates via e-values and is provably more sample-efficient than data splitting under explicit conditions.
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arXiv preprint arXiv:2208.02814 , year=
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A decomposition-based modular conformal prediction method for two-stage models with FWER-controlled stage-wise scaling and adaptive extension for non-stationary data.
SAVER proposes a conformal groundability gate plus submodular image selector that activates vision only when needed for multimodal named entity recognition and relation extraction, improving F1 while lowering compute.
RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.
ConfGuide uses conformal risk control to generate targeted guidance sets in a learning-to-guide hybrid decision framework and demonstrates it on multi-label medical diagnosis.
CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.
Pith review generated a malformed one-line summary.
A survey that categorizes uncertainty quantification approaches for graphical models into representation and handling dimensions to identify challenges and opportunities.
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Post-Selection Distributional Model Evaluation
PS-DME is a new framework that controls post-selection false coverage rate for distributional KPI estimates via e-values and is provably more sample-efficient than data splitting under explicit conditions.
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Decomposition-Based Modular Conformal Prediction for Two-Stage Modeling
A decomposition-based modular conformal prediction method for two-stage models with FWER-controlled stage-wise scaling and adaptive extension for non-stationary data.
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SAVER: Selective As-Needed Vision Evidence for Multimodal Information Extraction
SAVER proposes a conformal groundability gate plus submodular image selector that activates vision only when needed for multimodal named entity recognition and relation extraction, improving F1 while lowering compute.
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Uncertainty Quantification for LLM-based Code Generation
RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.
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Robust Conditional Conformal Prediction via Branched Normalizing Flow
Branched Normalizing Flow improves conditional coverage robustness of conformal prediction under distribution shift by normalizing test inputs to the calibration distribution and mapping prediction sets back.
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Geometry-Calibrated Conformal Abstention for Language Models
Geometry-calibrated conformal abstention lets language models abstain from uncertain queries with finite-sample guarantees on both participation rate and conditional correctness of answers.
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On a Probability Inequality for Order Statistics with Applications to Bootstrap, Conformal Prediction, and more
An approximate inequality for the probability involving order statistics under near-i.i.d. conditions is established and applied to justify resampling-based statistical procedures.
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Hybrid Decision Making via Conformal VLM-generated Guidance
ConfGuide uses conformal risk control to generate targeted guidance sets in a learning-to-guide hybrid decision framework and demonstrates it on multi-label medical diagnosis.
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Explainable Wastewater Digital Twins: Adaptive Context-Conditioned Structured Simulators with Self-Falsifying Decision Support
CCSS-IX is a context-conditioned structured simulator for wastewater digital twins that uses adaptive expert mixing and self-falsifying conformal decision rules to reduce unsafe actions while maintaining low prediction error on real plant and benchmark data.
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A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification
Pith review generated a malformed one-line summary.
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Uncertainty Quantification on Graph Learning: A Survey
A survey that categorizes uncertainty quantification approaches for graphical models into representation and handling dimensions to identify challenges and opportunities.
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