Modularized Implementation of Deep RL Algorithms in PyTorch
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Updated
Apr 16, 2024 - Python
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
Conformalized Quantile Regression
Conformal classifiers, regressors and predictive systems
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 th…
Quantile Regression Forests compatible with scikit-learn.
R package for Bayesian meta-analysis models, using Stan
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 flexi…
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
Our implementation of the paper "A Multi-Horizon Quantile Recurrent Forecaster"
Deep joint mean and quantile regression for spatio-temporal problems
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.
Valid and adaptive prediction intervals for probabilistic time series forecasting
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).
Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
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