Modularized Implementation of Deep RL Algorithms in PyTorch

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

Python package for conformal prediction

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

Conformalized Quantile Regression

Quantile Regression Forests compatible with scikit-learn.

Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.

👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster

Valid and adaptive prediction intervals for probabilistic time series forecasting.

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexi…

Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.

Bringing back uncertainty to machine learning.

R package for Bayesian meta-analysis models, using Stan

Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence

Using an integrated pinball-loss objective function in various recurrent based deep learning architectures made with keras to simultaneously produce probabilistic forecasts for UK wind, solar, demand and price forecasts.

A Julia package for robust regressions using M-estimators and quantile regressions

Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost

Our implementation of the paper "A Multi-Horizon Quantile Recurrent Forecaster"

Lasso Quantile Regression

R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).