HAPS constructs shorter conformal prediction sets for censored time-to-event outcomes by using time-varying covariate histories and IPCW, achieving approximate coverage among survivors with up to 75% shorter intervals in simulations.
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12 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 12verdicts
UNVERDICTED 12representative citing papers
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
Derives a closed-form impossibility bound and feasibility test for conformal risk control on structured LLM outputs, with empirical comparison of bounds and adaptive inference across models and tasks.
MSIFR stops faulty LLM generations early via staged rule-based checks, reducing token consumption 11-78% with no accuracy loss.
CPR uses query-level conformal calibration over path scores and a PUCT-trained RCVNet to achieve valid coverage guarantees and smaller prediction sets in KGQA, reporting 45% higher empirical coverage and 52% smaller sets than prior conformal baselines.
Safety certification of dynamical systems is reformulated as direct classification via kernel embeddings on trajectories, bypassing recursive DP to avoid error compounding and support non-Markovian dynamics.
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
Standard conformal prediction gives nominal overall coverage on Pew survey data but leaves ~13-point weighted gaps across race-education subgroups, and group-specific Mondrian calibration does not reliably close them.
QpiGNN provides a quantile-free dual-head architecture for GNN uncertainty quantification that directly optimizes coverage and interval width, yielding 22% higher coverage and 50% narrower intervals than baselines on 19 benchmarks with asymptotic coverage guarantees under mild assumptions.
SUA measures the gap between how much an LLM's output changes under perturbations and how uncertain the model claims to be, with a training procedure to reduce that gap.
Adapts conformal prediction methods to provide distribution-free uncertainty quantification and coverage guarantees for continuous evaluation of AI agent quality scores.
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.
citing papers explorer
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History-Aware Conformal Prediction Sets for Censored Time-to-Event Outcomes
HAPS constructs shorter conformal prediction sets for censored time-to-event outcomes by using time-varying covariate histories and IPCW, achieving approximate coverage among survivors with up to 75% shorter intervals in simulations.
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Super-Level-Set Regression: Conditional Quantiles via Volume Minimization
Super-level-set regression directly optimizes conditional level-set boundaries via volume minimization to achieve minimum-volume prediction regions with conditional coverage.
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When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
Derives a closed-form impossibility bound and feasibility test for conformal risk control on structured LLM outputs, with empirical comparison of bounds and adaptive inference across models and tasks.
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Know When To Fold 'Em: Token-Efficient LLM Synthetic Data Generation via Multi-Stage In-Flight Rejection
MSIFR stops faulty LLM generations early via staged rule-based checks, reducing token consumption 11-78% with no accuracy loss.
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Conformal Path Reasoning: Trustworthy Knowledge Graph Question Answering via Path-Level Calibration
CPR uses query-level conformal calibration over path scores and a PUCT-trained RCVNet to achieve valid coverage guarantees and smaller prediction sets in KGQA, reporting 45% higher empirical coverage and 52% smaller sets than prior conformal baselines.
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Safety Certification is Classification
Safety certification of dynamical systems is reformulated as direct classification via kernel embeddings on trajectories, bypassing recursive DP to avoid error compounding and support non-Markovian dynamics.
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A Conformal Selection Framework for Individual Treatment Beneficiaries with Auxiliary External Data
A model-agnostic conformal selection method reformulates CATE-based beneficiary identification as multiple testing with RCT-calibrated p-values and FDR control, allowing external data for model training.
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Socio-Conformal Calibration in Complex Survey Data: Marginal Validity Is Not Enough for Subgroup Reliability
Standard conformal prediction gives nominal overall coverage on Pew survey data but leaves ~13-point weighted gaps across race-education subgroups, and group-specific Mondrian calibration does not reliably close them.
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Quantile-Free Uncertainty Quantification in Graph Neural Networks
QpiGNN provides a quantile-free dual-head architecture for GNN uncertainty quantification that directly optimizes coverage and interval width, yielding 22% higher coverage and 50% narrower intervals than baselines on 19 benchmarks with asymptotic coverage guarantees under mild assumptions.
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Sensitivity Uncertainty Alignment in Large Language Models
SUA measures the gap between how much an LLM's output changes under perturbations and how uncertain the model claims to be, with a training procedure to reduce that gap.
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Distribution-Free Uncertainty Quantification for Continuous AI Agent Evaluation
Adapts conformal prediction methods to provide distribution-free uncertainty quantification and coverage guarantees for continuous evaluation of AI agent quality scores.
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Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT
Tutorial on a statistical roadmap and R packages for selective borrowing in hybrid controlled trials, demonstrated on synthetic lung cancer data.