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

Conformalized Quantile Regression

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

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 flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

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) and preventing quantile crossing using LightGBM and XGBoost

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

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

R code for CAViaR model

Lasso Quantile Regression

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

PyTorch - Implicit Quantile Networks - Quantile Regression - C51

Slides and notebooks for my tutorial at PyData London 2018

Functions to calculate student growth percentiles and percentile growth projections/trajectories for students using large scale, longitudinal assessment data. Functions use quantile regression to estimate the conditional density associated with each student's achievement history. Percentile growth projections/trajectories are calculated using the coefficient matrices derived from the quantile regression analyses and specify what percentile growth is required for students to reach future achievement targets.

Deep joint mean and quantile regression for spatio-temporal problems

Measure market risk by CAViaR model

The repository gives case studies on short-term traffic flow forecasting strategies within the scope of my master thesis.

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

VAE + Quantile Networks for MNIST