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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.

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4 results for “epistemic-aware”

Variance-Gated Ensembles: An Epistemic-Aware Framework for Uncertainty Estimationcs.LG

VGE provides a practical approach to uncertainty estimation in ensemble models.

problem Uncertainty estimation in ensemble models using additive decomposition breaks down.

method Variance-Gated Ensembles (VGE) introduces a differentiable framework with a signal-to-noise gate.

result VGE provides a Variance-Gated Margin Uncertainty (VGMU) score and Variance-Gated Normalization (VGN) layer.

uncertainty estimation ensemble models epistemic-aware signal-to-noise differentiable

2026-02-08abs ↗pdf ↗

CREDO: Epistemic-Aware Conformalized Credal Envelopes for Regressionstat.ML

CREDO combines credal and conformal methods to create interpretable prediction intervals.

problem Overconfident prediction intervals in regions of model extrapolation.

method CREDO uses a credal envelope to widen intervals in weak evidence regions and then applies conformal calibration.

result CREDO prediction intervals are interpretable and maintain target coverage.

conformal prediction credal methods epistemic uncertainty regression prediction intervals

2026-03-06abs ↗pdf ↗

A neurosymbolic Approach with Epistemic Deep Learning for Hierarchical Image Classificationcs.CV

Unified framework for hierarchical image classification with epistemic uncertainty.

problem Overconfident predictions and lack of logical consistency in deep learning models.

method Neurosymbolic approach with epistemic deep learning, using focal set reasoning and differentiable fuzzy logic.

result Maintains accuracy on par with transformer baselines while providing more calibrated and interpretable predictions.

Hierarchical Image Classification Epistemic Uncertainty Neurosymbolic Focal Set Reasoning Differentiable Fuzzy Logic

2026-05-11abs ↗pdf ↗

Active Inference and Epistemic Value in Graphical Modelsstat.ML

This paper introduces a new approach to active inference using constrained Bethe Free Energy.

problem Tackling the limitations of existing epistemic behavior models in active inference.

method Introducing a constrained Bethe Free Energy (CBFE) perspective to optimize epistemic behavior in generative models.

result CBFE optimization leads to more robust and flexible epistemic behavior compared to existing methods.

Active Inference Epistemic Behavior Constrained Bethe Free Energy Message Passing Generative Models

2021-09-01abs ↗pdf ↗