4 papers · 1 filter
Conformal Calibration for Multi-Modal Regression with Missing Modalities
Ilia Azizi
Prediction intervals for multi-modal regression with tabular variables, text, images, or other input sources are difficult to calibrate when those sources disagree or one is missin…
CLEAR: Calibrated Learning for Epistemic and Aleatoric Risk
Ilia Azizi, Juraj Bodik, Jakob Heiss +1
Accurate uncertainty quantification is critical for reliable predictive modeling. Existing methods typically address either aleatoric uncertainty due to measurement noise or episte…
SEMF: Supervised Expectation-Maximization Framework for Predicting Intervals
Ilia Azizi, Marc-Olivier Boldi, Valérie Chavez-Demoulin
This work introduces the Supervised Expectation-Maximization Framework (SEMF), a versatile and model-agnostic approach for generating prediction intervals with any ML model. SEMF e…
UncertaintyPlayground: A Fast and Simplified Python Library for Uncertainty Estimation
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 trainin…