There are 3 repositories under quantile-regression topic.
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
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.
A Julia package for robust regressions using M-estimators and quantile regressions
Our implementation of the paper "A Multi-Horizon Quantile Recurrent Forecaster"
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.
Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost
R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
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.
NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'
Measure market risk by CAViaR model
This repository contains python implementations of scoring rules for forecasts provided in a prediction interval format.
Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net
R Package. Bayesian and nonparametric quantile regression, using Gaussian Processes to model the trend, and Dirichlet Processes, for the error. Author: Carlos Omar Pardo Gomez.
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.
This is the R code for several common non-parametric methods (kernel est., mean regression, quantile regression, boostraps) with both practical applications on data and simulations
Open-source implementation of ADMM algorithms for penalized quantile regression in Gu, et al. 2018 Technometrics