Projects in Awesome Lists tagged with quantile-regression

https://github.com/shangtongzhang/deeprl

Modularized Implementation of Deep RL Algorithms in PyTorch

a2c categorical-dqn ddpg deep-reinforcement-learning deeprl double-dqn dqn dueling-network-architecture option-critic option-critic-architecture ppo prioritized-experience-replay pytorch quantile-regression rainbow td3

Last synced: 13 Apr 2025

https://github.com/ShangtongZhang/DeepRL

Modularized Implementation of Deep RL Algorithms in PyTorch

a2c categorical-dqn ddpg deep-reinforcement-learning deeprl double-dqn dqn dueling-network-architecture option-critic option-critic-architecture ppo prioritized-experience-replay pytorch quantile-regression rainbow td3

Last synced: 01 Apr 2025

https://github.com/qfettes/deeprl-tutorials

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

a2c actor-critic advantage-actor-critic categorical-dqn deep-q-network deep-recurrent-q-network deep-reinforcement-learning deeprl-tutorials double-dqn dueling-dqn gae multi-step-learning noisy-networks ppo prioritized-experience-replay python3 pytorch quantile-regression rainbow reinforcement-learning

Last synced: 16 May 2025

https://github.com/qfettes/DeepRL-Tutorials

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

a2c actor-critic advantage-actor-critic categorical-dqn deep-q-network deep-recurrent-q-network deep-reinforcement-learning deeprl-tutorials double-dqn dueling-dqn gae multi-step-learning noisy-networks ppo prioritized-experience-replay python3 pytorch quantile-regression rainbow reinforcement-learning

Last synced: 01 May 2025

https://github.com/henrikbostrom/crepes

Python package for conformal prediction

confidence-intervals conformal-classifiers conformal-prediction conformal-predictive-systems conformal-regressors cumulative-distribution-function jupyter-notebook machine-learning prediction-intervals prediction-sets python quantile-regression regression scikit-learn sklearn uncertainty-quantification

Last synced: 16 May 2025

https://github.com/yromano/cqr

Conformalized Quantile Regression

algorithmic-fairness conformal-methods conformal-prediction deep-learning fairness prediction prediction-intervals pytorch quantile-regression random-forest random-forest-regression

Last synced: 15 Mar 2025

https://github.com/zillow/quantile-forest

Quantile Regression Forests compatible with scikit-learn.

machine-learning prediction-intervals python quantile-regression quantile-regression-forests random-forest scikit-learn-api uncertainty-estimation

Last synced: 05 Apr 2025

https://github.com/eliahuhorwitz/conffusion

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

computer-vision conformal-prediction deep-learning diffusion diffusion-models generative-model image-generation image-to-image inpainting prediction-intervals pytorch quantile-regression super-resolution uncertainty-estimation uncertainty-quantification

Last synced: 01 Aug 2025

https://github.com/superlinear-ai/conformal-tights

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

conformal-prediction forecasting machine-learning prediction-intervals python quantile-regression scikit-learn time-series-forecasting

Last synced: 11 Oct 2025

https://github.com/liquidSVM/liquidSVM

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.

apache-spark c-plus-plus classification expectile-regression machine-learning matlab ml octave python quantile-regression r r-package regression rstats svm

Last synced: 15 Mar 2025

https://github.com/FilippoMB/Ensemble-Conformalized-Quantile-Regression

Valid and adaptive prediction intervals for probabilistic time series forecasting

conformal-prediction ensemble-machine-learning frequentist-statistics frequentistic-confidence-intervals prediction-intervals probabilistic-forecasting quantile-regression random-forest recurrent-neural-networks time-series-forecasting time-series-prediction uncertainty-quantification

Last synced: 11 May 2025

https://github.com/liquidsvm/liquidsvm

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.

apache-spark c-plus-plus classification expectile-regression machine-learning matlab ml octave python quantile-regression r r-package regression rstats svm

Last synced: 23 Feb 2025

https://github.com/saattrupdan/doubt

Bringing back uncertainty to machine learning.

bootstrap confidence-intervals machine-learning prediction-intervals quantile-regression quantile-regression-forests uncertainty

Last synced: 12 Sep 2025

https://github.com/wwiecek/baggr

R package for Bayesian meta-analysis models, using Stan

bayesian-statistics meta-analysis quantile-regression stan treatment-effects

Last synced: 26 Feb 2026

https://github.com/getzze/robustmodels.jl

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

expectile-regression julia-language m-estimator mm-estimator mquantile-regression outlier-detection outlier-rejection quantile-regression ridge-regression robust-regression s-estimator tau-estimator

Last synced: 11 Mar 2026

https://github.com/centerforassessment/sgp

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.

cran percentile-growth-projections quantile-regression r sgp sgp-analyses student-growth-percentiles student-growth-projections