Modularized Implementation of Deep RL Algorithms in PyTorch

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

A library for ready-made reinforcement learning agents and reusable components for neat prototyping

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.

Conformalized Quantile Regression

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.

PyTorch - Implicit Quantile Networks - Quantile Regression - C51

VAE + Quantile Networks for MNIST

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

Lasso Quantile Regression

Deep joint mean and quantile regression for spatio-temporal problems

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.

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

Regularization Paths for Huber Loss Regression and Quantile Regression Penalized by Lasso or Elastic-Net

R code for CAViaR model

A validation study for the application of quantile regression neural networks to Bayesian remote sensing retrievals

Bringing back uncertainty to machine learning.

censored Quantile Random Forest

Online random forests with prediction uncertainty

Qauntile autoregressive neural network for time series anamoly detection.

Stata implementation of the Athey and Imbens (2006) Changes-in-Changes model

Economics C142: Applied Econometrics / Spring 2017 / UC Berkeley

censored quantile regression with three-step estimator

This repository contains python implementations of scoring rules for forecasts provided in a prediction interval format.

test the phenomenon of PEAD in China

Random or Extremely Random Forest for censored quantile regression.

Recreation of the findings presented in the paper titled "Parsimonious Quantile Regression of Financial Asset Tail Dynamics via Sequential Learning"

The goal of esreg is to simultaneously model the quantile and the expected shortfall of a response variable given a set of covariates.