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

Natural Gradient Boosting for Probabilistic Prediction

A Library for Uncertainty Quantification.

Lightweight, useful implementation of conformal prediction on real data.

Curated list of open source tooling for data-centric AI on unstructured data.

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

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

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

An extension of XGBoost to probabilistic modelling

A library for Bayesian neural network layers and uncertainty estimation in Deep Learning extending the core of PyTorch

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

A generic Mixture Density Networks (MDN) implementation for distribution and uncertainty estimation by using Keras (TensorFlow)

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

An extension of LightGBM to probabilistic modelling

CVPR 2020 - On the uncertainty of self-supervised monocular depth estimation

[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation

[CVPR 2022 Oral] Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometry

Quantile Regression Forests compatible with scikit-learn.

Code for the Neural Processes website and replication of 4 papers on NPs. Pytorch implementation.

[TMECH'2024] Official codes of the paper. PALoc: Advancing SLAM Benchmarking with Prior-Assisted 6-DoF Trajectory Generation and Uncertainty Estimation