On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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← all fields·12 papers on “conformal prediction” in Statistical ML · 1 month

Isotonic Conformal Predictionstat.ML

ICP improves prediction intervals for continuous outcomes at lower computational cost.

problem Systematic bias in point predictions that undermines their use in decision-making.

method Develops Isotonic Conformal Prediction (ICP) framework to decouple calibration from prediction-set construction.

result SICP and TICP procedures match SC-CP coverage at lower computational cost.

conformal prediction isotonic regression calibration prediction intervals heteroscedastic regression

2026-07-18abs ↗pdf ↗

Manifold Constrained Conformal Prediction for Spatial Eventsstat.ML

New method predicts spatial events like hurricanes and earthquakes with uncertainty.

problem Quantifying uncertainty in natural hazard predictions.

method Representing spatial point clouds as empirical measures, constraining prediction sets to spatial data manifold, using Wasserstein distance.

result Achieves near-nominal coverage and lower energy/manifold distances compared to baselines.

conformal prediction spatial events natural hazards tropical cyclones earthquakes

2026-07-10abs ↗pdf ↗

A Quiet Failure in Calibrated Virtual Screening: Marginal Conformal Prediction Under-Covers the Minority Class, and a Class-Conditional Fix Recovers Itcs.LG

Conformal prediction fails to cover minority classes in imbalanced datasets, but a class-conditional fix improves coverage.

problem Conformal prediction fails to cover minority classes in imbalanced datasets, leading to poor performance on rare labels.

method Class-conditional conformal prediction to improve coverage of minority classes.

result Class-conditional conformal prediction restores minority coverage to target with a modest increase in prediction-set size.

conformal prediction imbalanced datasets minority class class-conditional coverage

2026-07-07abs ↗pdf ↗

Robust Bayes-Assisted Conformal Predictionstat.ML

Robust Bayes-Assisted Conformal Prediction improves prediction set sizes.

problem Misspecification of Bayesian working model and prior misalignment.

method RoBAS (Robust Bayes-Assisted Shrinkage) framework with two instantiations.

result Improves prediction set sizes in shifted settings.

Bayesian Conformal Prediction Robustness Shrinkage Tabular Data

2026-07-05abs ↗pdf ↗

Robust Bayes-Assisted Conformal Predictionstat.ML

Robust Bayes-Assisted Conformal Prediction improves prediction set sizes.

problem Misspecification of Bayesian working model and prior misalignment.

method RoBAS (Robust Bayes-Assisted Shrinkage) framework with two instantiations.

result Proposed scores adapt to prior quality, reducing interval widths in shifted settings.

Bayesian Conformal Prediction Robustness Shrinkage Nonconformity Scores

2026-07-05abs ↗pdf ↗

Prediction Sets for Counterfactual Decisions: Coverage, Optimality, and Conformal Predictionstat.ML

Develops optimal decision-making framework for uncertain counterfactuals.

problem Ensuring reliability of predictions in high-stakes decisions.

method Policy-Coupled Risk-Averse Conformal Prediction (PC-RACP).

result Optimal prediction sets for counterfactual decisions with valid coverage.

counterfactual decisions policy-coupled coverage conformal prediction max-min rule universal coverage

2026-07-02abs ↗pdf ↗

Accelerating Conformal Prediction via Approximate Leave-One-Outstat.ML

Paper accelerates conformal prediction by using approximate leave-one-out estimators.

problem Limited computational cost for conformal prediction.

method Incorporates approximate leave-one-out estimators to accelerate conformal prediction.

result ALO-based methods achieve comparable coverage and efficiency to exact methods but with significantly reduced runtime.

conformal prediction leave-one-out uncertainty quantification approximate methods runtime reduction

2026-06-30abs ↗pdf ↗

Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Modelscs.LG

STOIC improves energy demand forecasting with reliable uncertainty estimates.

problem Accurate point forecasts alone are insufficient for energy systems; reliable uncertainty estimates are needed.

method Integrates graph-based forecasting with tabular foundation models for zero-shot calibration of spatial-temporal residuals.

result STOIC delivers more reliable and robust uncertainty estimates for complex graph-structured energy time series.

energy forecasting uncertainty quantification graph neural networks conformal prediction tabular foundation models

2026-06-30abs ↗pdf ↗

On Optimal Data Splitting for Split Conformal Predictionmath.ST

Optimizes data splitting for shorter conformal prediction intervals.

problem Minimizing prediction interval length while maintaining coverage.

method Theoretical framework for optimal data splitting in split conformal prediction.

result Analytical characterizations of length-optimal split ratios in various settings.

conformal prediction data splitting uncertainty quantification prediction intervals statistical efficiency

2026-06-30abs ↗pdf ↗

Accelerometry-Derived Digital Biomarkers for Cardiometabolic Risk: A Population-Representative Tabular Benchmark with Uncertainty Quantificationcs.LG

New benchmark predicts cardiometabolic risk from accelerometer data, with varying accuracy.

problem Lack of accurate tabular benchmarks for cardiometabolic risk from accelerometer data.

method Tabular learning methods (ridge regression, XGBoost, TabPFN v2) applied to NHANES data.

result TabPFN v2 achieves best performance, but triglycerides remain largely unpredictable.

accelerometry cardiometabolic risk benchmark tabular learning NHANES

2026-06-29abs ↗pdf ↗

Self-Organized Conformal Prediction: Reducing Regional Coverage Gaps with Unsupervised Group Discoverystat.ML

SOCP uses SOM to find groups and local calibration buffers for better regional coverage.

problem Heterogeneous regional coverage gaps in conformal prediction.

method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.

result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.

conformal prediction regional coverage Self-Organizing Map local calibration group discovery

2026-06-28abs ↗pdf ↗

Conformal Prediction with Macro-Coverage Guaranteesstat.ME

New method uses label-weighted conformal prediction for macro-coverage guarantees in classification.

problem Finding a balance between class-conditional and marginal coverage in long-tailed datasets.

method Label-weighted conformal prediction for macro-coverage guarantees.

result Validated prediction sets with macro-coverage guarantees on large-scale image datasets.

conformal prediction macro-coverage long-tailed data classification

2026-06-26abs ↗pdf ↗