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?.

Epistemic AlphaZero utilizes uncertainty to explore and learn even when AlphaZero gets stuck.

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

Decoder-only LLM with integrated epistemic tomography. Knows what it doesn't know.

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).

Implementation of Epistemic Time-Dilation MAPPO (ETD-MAPPO). A compute-aware MARL framework where agents autonomously modulate their execution frequency based on uncertainty to reduce inference overhead.

Energy production forecasting ⚡ with PoC of Bayesian Neural Network 🎲

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

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

A Python-based tutorial on quantifying epistemic uncertainty from sparse literature data. Compares Interval Analysis, Probabilistic Mixture Models, and Dempster-Shafer Evidence Theory using a Battery GWP case study.

Generalized Automatic Pipeline for inspecting and fixing uncertainties in your data

Limits of Acceptability with DREAM algorithm: MATLAB and Python code

A generative neural network approach to uncertainty and risk-return analysis in mineral prospectivity modelling

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

Selecting fitted models under epistemic uncertainty [Source Code]

AI research for bias mitigation and truth verification - building fair, transparent systems that combat misinformation while ensuring equitable outcomes across all demographics.