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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·14 papers on “uncertainty quantification” in Statistical ML · 1 month

Uncertainty quantification in mechanics: A unified Bayesian perspectivephysics.comp-ph

Unified Bayesian framework for uncertainty quantification in mechanics.

problem Propagation of input uncertainties and inference of unknown parameters.

method Bayesian probability theory for forward and inverse problems.

result Unified theoretical framework for both forward and inverse UQ.

Bayesian probability uncertainty quantification mechanics forward problem inverse problem

2026-07-21abs ↗pdf ↗

Calibrating Semantic Uncertainty from Observable Language-Model Probabilitiesstat.ME

The semantic map calibrates uncertainty from language model probabilities.

problem Uncertainty in language model probabilities for professional decisions.

method Prespecified semantic map linking probabilities of verbal responses to probabilities of declared states.

result Language-derived probabilities outperform printed numerical probabilities and recover valid uncertainty coverage.

semantic uncertainty language model calibration posterior inference professional decisions

2026-07-20abs ↗pdf ↗

Subjective Risk Decomposition: A New View for Uncertainty Quantificationstat.ML

A new method decomposes subjective risk into epistemic and aleatoric uncertainties.

problem Uncertainty quantification in modeling decisions.

method Subjective risk decomposition using strictly proper loss.

result Recovery of classic uncertainty measures and new learning-theoretic connections.

uncertainty quantification subjective risk epistemic uncertainty aleatoric uncertainty strictly proper loss

2026-07-16abs ↗pdf ↗

Operator-Informed Gaussian Processes for Complex Helmholtz Wavefields: From Synthetic Benchmarks to In Vivo Brain Elastographystat.ML

Extends GP regression to complex Helmholtz problems, improving wavefield inference in brain elastography.

problem Infer complex Helmholtz wavefields from sparse, noisy data.

method Operator-informed Gaussian processes, realifying complex operator into real blocks, using PDE residuals and boundary traces.

result Competitive with finite-difference and neural-network methods, reconstructs brain shear curl field with high correlation.

Gaussian processes Helmholtz equation complex modulus brain elastography wavefield inference

2026-07-15abs ↗pdf ↗

TSCoNet: A Two-Stage Copula CNN-LSTM for Uncertainty-Aware Spatio-Temporal Forecastingstat.ML

TSCoNet forecasts correlated geophysical fields with uncertainty estimates.

problem Accurate and reliable forecasts of correlated geophysical fields across many locations.

method Two-stage CNN-LSTM coupled with Gaussian copula.

result Calibrated prediction intervals without sacrificing point accuracy.

deep learning spatio-temporal forecasting uncertainty quantification copula convolutional neural networks

2026-07-11abs ↗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 ↗

Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Datastat.ML

Integrates neural encoders into GLMMs for multimodal data analysis.

problem Scalable Bayesian inference for GLMMs assumes low-dimensional tabular predictors and does not handle high-dimensional modalities.

method Jointly learns modality-specific neural encoders with GLMM objective, performs variance-corrected stochastic-gradient MCMC.

result Preserves interpretable fixed and random effects while scaling to large longitudinal datasets.

Bayesian inference Generalized Linear Mixed Models Neural Encoders Multimodal Data Uncertainty Quantification

2026-07-06abs ↗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 ↗

Sequential sparse Gaussian process quantile regressioncs.LG

Sparse Gaussian process quantile regression tackles computational challenges in Bayesian quantile regression.

problem Nonconjugacy and computational cost in Gaussian process quantile regression.

method Sparse Gaussian process framework with Laplace approximation, adaptive inducing-input placement, and sequential data acquisition.

result Accuracy of Laplace approximation and effectiveness of adaptive mechanisms in reducing predictive uncertainty.

quantile regression Gaussian process Bayesian inference adaptive sampling sequential algorithm

2026-06-30abs ↗pdf ↗

A Bayesian latent Gaussian process framework for aerodynamic uncertainty quantificationstat.ML

Bayesian framework predicts aerodynamic uncertainty from sparse measurements.

problem Calibrating aerodynamic models with sparse and uncertain measurements.

method Bayesian latent Gaussian process for surrogate model calibration.

result Calibrated surrogate model accurately predicts aerodynamic uncertainty.

aerodynamics uncertainty quantification Gaussian process calibration surrogate modeling

2026-06-27abs ↗pdf ↗

Ribbon: Scalable Approximation and Robust Uncertainty Quantificationstat.ML

Ribbon: Scalable Approximation and Robust Uncertainty Quantification

problem Reliably quantifying predictive uncertainty for complex models

method Ribbon, a scalable approximation to Dirichlet-reweighted bootstrap uncertainty

result Asymptotically equivalent to a flat-prior Laplace approximation under correct likelihood specification, recovers robust sandwich covariance under misspecification

Ribbon Dirichlet-reweighted bootstrap uncertainty quantification scalable

2026-06-25abs ↗pdf ↗

Decision-Aligned Evaluation of Uncertainty Quantificationcs.LG

Decision-alignment evaluates uncertainty quantification for decision-relevant UQ

problem Evaluation of uncertainty quantification metrics

method Introduce decision-alignment

result Proper scoring rules align with decision utility

decision-alignment uncertainty quantification proper scoring rules decision utility

2026-06-25abs ↗pdf ↗