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22 search results found

Deeprl ⭐ 2,834

Modularized Implementation of Deep RL Algorithms in PyTorch

Deeprl Tutorials ⭐ 726

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

Rlcycle ⭐ 242

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

Conformal classifiers, regressors and predictive systems

Conformalized Quantile Regression

Quantile Forest ⭐ 70

Quantile Regression Forests compatible with scikit-learn.

Liquidsvm ⭐ 45

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-s

Bringing back uncertainty to machine learning.

R package for Bayesian meta-analysis models, using Stan

Conffusion ⭐ 37

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

Ensemble Conformalized Quantile Regression ⭐ 31

Valid and adaptive prediction intervals for probabilistic time series forecasting

Im2im Uq ⭐ 29

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.

Robustmodels.jl ⭐ 28

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

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

Pydata London 2018 ⭐ 20

Slides and notebooks for my tutorial at PyData London 2018

Dqn Pytorch ⭐ 11

PyTorch - Implicit Quantile Networks - Quantile Regression - C51

R code for CAViaR model

VAE + Quantile Networks for MNIST

Quantregforest ⭐ 8

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

Lasso Quantile Regression

Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems

Partially-Interpretable Neural Networks for Extreme Value modelling

Related Searches

R Quantile Regression (13)

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