Modularized Implementation of Deep RL Algorithms in PyTorch

Python package for conformal prediction

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

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

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.

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.

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

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

Lasso Quantile Regression

Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems

PyTorch - Implicit Quantile Networks - Quantile Regression - C51

VAE + Quantile Networks for MNIST

Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.

Qauntile autoregressive neural network for time series anamoly detection.

Regression algorithm implementaion from scratch with python (OLS, LASSO, Ridge, robust regression)

Python package with a class that allows pipeline-like specification and execution of regression workflows.

The fastest and most accurate methods for quantile regression, now in Python.