Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

Machine learning models for estimating aleatoric and epistemic uncertainty with evidential and ensemble methods.

NOMU: Neural Optimization-based Model Uncertainty

[ACL 2025] Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty?.

This repository contains a demontstration of how to build, train and evaluate a neural network capable of measuring epistemic uncertainty as proposed by the authors of Evidential Deep Learning to Quantify Classification Uncertainty

Energy production forecasting ⚡ with PoC of Bayesian Neural Network 🎲

Work as part of ANL summer 2020 research on uncertainity quanitification methods in graph neural networks

Uncertainty quantification and out-of-distribution detection using surjective normalizing flows

Generalized Automatic Pipeline for inspecting and fixing uncertainties in your data

Original implementation of the EMD (empirical model discrepancy) model comparison criterion

Limits of Acceptability with DREAM algorithm: MATLAB and Python code

the prior distribution for probabilistic numerical methods

Code for the ICASSP'19 submission "Modelling Sample Informativeness for Deep Affective Computing".

A framework for mapping the internal geometry of transformer representations using angular projection, neuron-level modulation, and epistemically grounded prompts. Based on and extending Bird's original Spotlight Resonance Method (SRM).