quantile-regression

There are 3 repositories under quantile-regression topic.

ShangtongZhang / DeepRL
Modularized Implementation of Deep RL Algorithms in PyTorch
pytorch deep-reinforcement-learning dqn dueling-network-architecture double-dqn deeprl ddpg ppo categorical-dqn option-critic quantile-regression td3 a2c option-critic-architecture prioritized-experience-replay rainbow
Language:Python 3276
qfettes / DeepRL-Tutorials
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
python3 pytorch reinforcement-learning deep-reinforcement-learning deep-q-network double-dqn multi-step-learning dueling-dqn noisy-networks prioritized-experience-replay deeprl-tutorials categorical-dqn rainbow quantile-regression deep-recurrent-q-network actor-critic advantage-actor-critic a2c gae ppo
Language:Jupyter Notebook 1059
crepes
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
Language:Python 482
cyoon1729 / RLcycle
A library for ready-made reinforcement learning agents and reusable components for neat prototyping
reinforcement-learning pytorch a2c ddpg hydra a3c dqn quantile-regression sac rainbow-dqn
Language:Python 300
yromano / cqr
Conformalized Quantile Regression
conformal-prediction quantile-regression deep-learning pytorch random-forest-regression prediction prediction-intervals fairness conformal-methods algorithmic-fairness random-forest
Language:Jupyter Notebook 264
zillow / quantile-forest
Quantile Regression Forests compatible with scikit-learn.
python machine-learning random-forest quantile-regression quantile-regression-forests scikit-learn-api uncertainty-estimation prediction-intervals
Language:Python 227
Conffusion
eliahuhorwitz / Conffusion
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
computer-vision conformal-prediction image-generation image-to-image inpainting quantile-regression super-resolution diffusion-models generative-model deep-learning prediction-intervals pytorch uncertainty-estimation uncertainty-quantification diffusion
Language:Python 139
superlinear-ai / conformal-tights
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
conformal-prediction machine-learning python quantile-regression scikit-learn prediction-intervals time-series-forecasting forecasting
Language:Python 104
FilippoMB / Ensemble-Conformalized-Quantile-Regression
Valid and adaptive prediction intervals for probabilistic time series forecasting.
probabilistic-forecasting time-series-forecasting time-series-prediction uncertainty-quantification frequentist-statistics frequentistic-confidence-intervals recurrent-neural-networks quantile-regression conformal-prediction random-forest prediction-intervals ensemble-machine-learning
Language:Jupyter Notebook 88
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.
svm regression classification machine-learning python rstats ml quantile-regression expectile-regression r r-package apache-spark c-plus-plus matlab octave
Language:C++ 67
aangelopoulos / im2im-uq
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.
unet uncertainty uncertainty-quantification uncertainty-estimation quantile-regression image-to-image-translation biomedical-image-processing biomedical-data-science computational-biology computational-imaging mri machine-learning microscopy pytorch
Language:Python 55
saattrupdan / doubt
Bringing back uncertainty to machine learning.
uncertainty machine-learning prediction-intervals quantile-regression quantile-regression-forests confidence-intervals bootstrap
Language:Python 51
wwiecek / baggr
R package for Bayesian meta-analysis models, using Stan
meta-analysis stan bayesian-statistics treatment-effects quantile-regression
Language:R 49
getzze / RobustModels.jl
A Julia package for robust regressions using M-estimators and quantile regressions
julia-language robust-regression quantile-regression ridge-regression m-estimator mm-estimator s-estimator tau-estimator expectile-regression mquantile-regression outlier-detection outlier-rejection
Language:Julia 36
ErezSC42 / qr_forcaster
Our implementation of the paper "A Multi-Horizon Quantile Recurrent Forecaster"
quantile-regression amazon time-series-forecasting
Language:Python 33
RichardFindlay / day-ahead-probablistic-forecasting-with-quantile-regression
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.
d3-visualization deep-learning interactive-visualizations keras pinball-loss quantile-regression renewable-energy cnn encoder-decoder lstm
Language:Python 33
BayerSe / l1qr
Lasso Quantile Regression
lasso value-at-risk quantile-regression
Language:Python 31
Buczman / CaviaR
R code for CAViaR model
value-at-risk quantile-regression caviar
Language:R 29
RektPunk / MQBoost
Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost
gradient-boosting lightgbm monotonicity-constraint quantile-estimation quantile-regression xgboost
Language:Python 29
lorismichel / quantregForest
R package - Quantile Regression Forests, a tree-based ensemble method for estimation of conditional quantiles (Meinshausen, 2006).
quantile-regression forest machine-learning
Language:C 27
dannysdeng / dqn-pytorch
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
implicit-quantile-networks pytorch quantile-regression c51 reinforcement-learning rainbow
Language:Python 23
msesia / chr
Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
machine-learning conformal-prediction regression uncertainty-quantification quantile-regression
Language:Python 22
gcampanella / pydata-london-2018
Slides and notebooks for my tutorial at PyData London 2018
optimization linear-programming convex-optimization quadratic-programming quantile-regression elastic-net survival-analysis maximum-likelihood-estimation multilayer-perceptron deep-learning gradient-descent stochastic-gradient-descent tutorial pydata
Language:Jupyter Notebook 21
SGP
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.
student-growth-percentiles r student-growth-projections sgp sgp-analyses percentile-growth-projections quantile-regression cran
Language:R 20
fmpr / DeepJMQR
Deep joint mean and quantile regression for spatio-temporal problems
deep-learning quantile-regression prediction-intervals spatio-temporal keras
Language:Jupyter Notebook 15
SSS135 / aiqn-vae
VAE + Quantile Networks for MNIST
python python3 pytorch mnist vae aiqn autoencoder neural-network deep-learning generative-model quantile-regression quantile-networks variational-autoencoder
Language:Python 12
TeaPearce / Censored_Quantile_Regression_NN
NeurIPS paper 'Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis'
deep-learning deep-neural-networks quantile-regression survival-analysis uncertainty-neural-networks uncertainty-quantification
Language:Jupyter Notebook 12
yatshunlee / CAViaR
Measure market risk by CAViaR model
risk-management caviar market-risk quantitative-finance quantitative-trading value-at-risk volatility-modeling quantile-regression backtesting var-forecasting dqtest dynamic-quantile-test var-backtesting
Language:Jupyter Notebook 11
adrian-lison / interval-scoring
This repository contains python implementations of scoring rules for forecasts provided in a prediction interval format.
forecasting scoring interval quantile quantile-regression
Language:Jupyter Notebook 10
CY-dev / hqreg
Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net
cran lasso elastic-net huber-loss-regression quantile-regression regularization-paths machine-learning-algorithms high-dimensional
Language:C 10
opardo / GPDPQuantReg
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.
rpackage quantile-regression bayesian-statistics nonparametric
Language:R 10
elifyilmaz2027 / traffic_flow_forecasting_methods
The repository gives case studies on short-term traffic flow forecasting strategies within the scope of my master thesis.
arima-models hybrid-models lstm qra qrnn quantile-regression seasonal-forecast svr traffic-flow-forecasting xgboost qrlstm
Language:Jupyter Notebook 9
Jbrich95 / pinnEV
Partially-Interpretable Neural Networks for Extreme Value modelling
deep-learning extreme-value-statistics keras quantile-regression regression-models tensorflow
Language:R 8
RektPunk / mcqrnn
Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.
monotonicity-constraint neural-network quantile-estimation quantile-regression tensorflow2
Language:Python 8
anhdanggit / non-parametric-econometrics
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
non-parametric bootstrap quantile-regression kernel density-estimation simulation
Language:R 7
fboehm / openFHDQR
Open-source implementation of ADMM algorithms for penalized quantile regression in Gu, et al. 2018 Technometrics
r r-package rstats admm quantile-regression
Language:C++ 6