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probly: Uncertainty-Aware Machine Learning

NeurIPS 2026 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: Epistemic versus aleatoric decomposition

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Introduces probly, a framework-agnostic toolkit for making machine learning models uncertainty-aware by unifying scattered uncertainty tools across libraries.

Problem

Making machine learning models uncertainty-aware is difficult because relevant tools are scattered across libraries, each tied to a specific framework and modeling approach.

Approach

A unified, framework-agnostic library that collects uncertainty representation and quantification methods behind one interface so that models can be made uncertainty-aware without rebuilding them for each framework or approach.

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