#Uncertainty-quantification
Showing 25 of 25 repositories tagged #uncertainty-quantification, ranked by stars
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 🌱
Probabilistic modelling and uncertainty quantification library
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).
Quantify uncertainty and sensitivities in your computer models with an industry-grade Monte Carlo library.
Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python
Official implementation of the AIAA Journal paper "Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models"
Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vision and natural language processing.
(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.
Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)
Helping AI practitioners better understand their datasets and models in text classification. From ServiceNow.
APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based components, and probabilistic modeling. Its modular design allows researchers and practitioners to experiment with multiple forecasting models and easily
Local explanations with uncertainty 💐!
[ICCV 2025 CVAMD] The official implementation of the paper "Prompt4Trust: A Reinforcement Learning Prompt Augmentation Framework for Clinically-Aligned Confidence Calibration in Multimodal Large Language Models".