conformal-prediction

This paper proposes Structure-Preserving Epistemic Neural Networks (S-PENNs), a framework for uncertainty quantification in scientific machine learning models with hard architectural constraints, instantiated for GENERIC dynamics to ensure thermodynamically consistent rollouts and calibrated prediction intervals with reduced computational cost.

Presents FunnelCausalNet, an uplift estimation method for multi-tier coupon allocation that couples conversion and revenue heads under a funnel composition to reduce variance, validated on semi-synthetic Criteo and industrial hotel-coupon RCT logs.

This paper introduces a cross-model map for certifying selective predictors that must meet both an automation floor and a risk ceiling under covariate shift, deriving feasibility frontiers and two-resource sample-complexity trade-offs.

Introduces Retrieval-Corrected Conformal Prediction (RCCP), a retrieval-augmented calibration method for time series prediction intervals that selects similar past residuals and applies a scalar conformal correction to achieve target coverage with low overhead.

This paper introduces Hybrid Probabilistic Zonotopes (HProbZ), a neural network output head that jointly models discrete modes, bounded drift, and stochastic noise with closed-form likelihood, enabling identifiable uncertainty decomposition, observation-driven contraction, and multi-modal conformal coverage.

SafeCommit presents a risk-controlled layer that certifies when memory-grounded LLM agents may safely act, using conformal action certificates to bound unsafe commitment probability and offering a simulator with public code.

A research paper introduces a recurrent neural operator to forecast tipping points in non-stationary dynamical systems, applied to climate and aerodynamics, with uncertainty quantification using conformal prediction. The tweet highlights the paper's utility for early detection of abrupt changes.

Introduces a conformalized split-sample framework for local model comparison, producing calibrated local best-model maps and finite-sample guarantees for declaring local superiority.

Introduces CALCoDe, a post-hoc reliability layer for frozen medical vision-language models that mitigates class-tail undercoverage under clinical shift, achieving strong worst-class accepted coverage across multiple dermatology shifts and VLM backbones.

This paper proposes weighted conformal methods for changepoint localization and root cause analysis that reduce confidence set size under corrupted observations by downweighting likely contaminated data, using uncertainty signals and meta-learning.

Presents a novel estimation framework for D-vine copulas using gradient-based MLE and beam search for better global fit, and a localized anomaly detection method with uncertainty quantification via conformal prediction.

This paper proposes a bootstrapping conformal prediction approach for constructing prediction intervals to improve right-sizing recommendations for virtual machines in data centers, enhancing cost efficiency and resource allocation.

This paper presents EaaS, a cloud-native microservices architecture for scalable AI monitoring that provides conformal prediction, calibration assessment, drift detection, and fairness monitoring with statistical guarantees.

This paper evaluates citation faithfulness in agentic scientific synthesis systems, showing that current verifiers are unreliable with unsupported-citation rates varying from 3% to 18% depending on strictness. It proposes a gold-anchored evaluation protocol and a deployable guard that uses split-conformal prediction to provide a distribution-free bound on truly unsupported citations.

Introduces Scientific Feasibility Control (SFC), a conformal prediction framework that provides statistical guarantees for scientific reasoning validity in LLMs, achieving 50.1% on PhyX physics reasoning, outperforming DeepSeek-R1 and GPT-4 while reducing scientific violations by 73%.

This paper reveals that standard marginal conformal prediction fails to cover minority classes in imbalanced virtual screening datasets, and demonstrates that class-conditional (Mondrian) conformal prediction restores per-class coverage.

This paper introduces a method for safe Bayesian optimization when safety is defined relative to a counterfactual baseline policy. It uses conformal prediction to estimate counterfactual outcomes and provides safety guarantees with user-specified violation rates.

SafeImpute proposes a reliable imputation framework for irregular clinical data using graph neural networks and conformal selection to control the false discovery rate of clinically unacceptable errors.

This paper addresses the challenge of predicting thermal volatility in high-performance EV powertrains under real-world loads by applying weighted conformal prediction, achieving modest improvements in coverage under covariate shift.

This paper introduces the NHANES Accelerometry Cardiometabolic Benchmark, a population-representative tabular dataset for predicting cardiometabolic risk from accelerometry data, and evaluates ridge regression, XGBoost, and TabPFN v2 with uncertainty quantification using conformal prediction.