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
Conformal classifiers, regressors and predictive systems
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.
Bringing back uncertainty to machine learning.
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.
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.
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
Slides and notebooks for my tutorial at PyData London 2018
R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).
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.
Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'
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.
Measure market risk by CAViaR model
This repository contains python implementations of scoring rules for forecasts provided in a prediction interval format.
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
D-Vine GAM Copula based Quantile Regression
R package for estimating quantile regression coefficients via the quantile spacing method