Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

UQLM: Uncertainty Quantification for Language Models, is a Python package for UQ-based LLM hallucination detection

Lightweight, useful implementation of conformal prediction on real data.

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

A Library for Uncertainty Quantification.

Awesome-LLM-Robustness: a curated list of Uncertainty, Reliability and Robustness in Large Language Models

This repository contains a collection of surveys, datasets, papers, and codes, for predictive uncertainty estimation in deep learning models.

A Python-based toolbox of various methods in decision making, uncertainty quantification and statistical emulation: multi-fidelity, experimental design, Bayesian optimisation, Bayesian quadrature, etc.

Literature survey, paper reviews, experimental setups and a collection of implementations for baselines methods for predictive uncertainty estimation in deep learning models.

Python package for conformal prediction

Open-source framework for uncertainty and deep learning models in PyTorch 🌱

Chaospy - Toolbox for performing uncertainty quantification.

A Python toolbox for conformal prediction research on deep learning models, using PyTorch.

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

DeepHyper: A Python Package for Massively Parallel Hyperparameter Optimization in Machine Learning

Probabilistic modelling and uncertainty quantification library

Next-generation camera-modeling toolkit

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).

RAVEN is a flexible and multi-purpose probabilistic risk analysis, validation and uncertainty quantification, parameter optimization, model reduction and data knowledge-discovering framework.

Uncertainpy: a Python toolbox for uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.

Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

Analysis of digital elevation models and elevation point clouds

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

A curated publication list on evidential deep learning.

tools for scalable and non-intrusive parameter estimation, uncertainty analysis and sensitivity analysis

Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.