Ilia Azizi

Ilia Azizi

This paper introduces UncertaintyPlayground, a Python library built on PyTorch and GPyTorch for uncertainty estimation in supervised learning tasks. The library offers fast training for Gaussian and multi-modal outcome distributions through Sparse and Variational Gaussian Process Regressions (SVGPRs) for normally distributed outcomes and Mixed Density Networks (MDN) for mixed distributions. In addition to model training with various hyperparameters, UncertaintyPlayground can visualize the prediction intervals of one or more instances. Due to using tensor operations, the library can be trained both on CPU and GPU and offers various PyTorch-specific techniques for speed optimization. The library contains unit tests for each module and ensures multi-platform continuous integration with GitHub Workflows (online integration) and Tox (local integration). Finally, the code is documented with Google-style docstrings and offers a documentation website created with MkDocs and MkDocStrings.

Ilia Azizi, Juraj Bodik, Jakob Heiss et al.

Existing methods typically address either aleatoric uncertainty due to measurement noise or epistemic uncertainty resulting from limited data, but not both in a balanced manner. We propose CLEAR, a calibration method with two distinct parameters, $γ_1$ and $γ_2$, to combine the two uncertainty components and improve the conditional coverage of predictive intervals for regression tasks. CLEAR is compatible with any pair of aleatoric and epistemic estimators; we show how it can be used with (i) quantile regression for aleatoric uncertainty and (ii) ensembles drawn from the Predictability-Computability-Stability (PCS) framework for epistemic uncertainty. Across 17 diverse real-world datasets, CLEAR achieves an average improvement of 28.2% and 17.4% in the interval width compared to the two individually calibrated baselines while maintaining nominal coverage. Similar improvements are observed when applying CLEAR to Deep Ensembles (epistemic) and Simultaneous Quantile Regression (aleatoric). The benefits are especially evident in scenarios dominated by high aleatoric or epistemic uncertainty.