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
A library for ready-made reinforcement learning agents and reusable components for neat prototyping
Conformal classifiers, regressors and predictive systems
Quantile Regression Forests compatible with scikit-learn.
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.
Our implementation of the paper "A Multi-Horizon Quantile Recurrent Forecaster"
Official Implementation for the "Conffusion: Confidence Intervals for Diffusion Models" paper.
Conformal Histogram Regression: efficient conformity scores for non-parametric regression problems
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.
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
Bringing back uncertainty to machine learning.
Multiple quantiles estimation model maintaining non-crossing condition (or monotone quantile condition) using LightGBM and XGBoost
PyTorch - Implicit Quantile Networks - Quantile Regression - C51
Qauntile autoregressive neural network for time series anamoly detection.
VAE + Quantile Networks for MNIST
Code and experiments related to the paper: 'Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference'
Monotone composite quantile regression neural network (MCQRNN) with tensorflow 2.x.
regression algorithm implementaion from scratch with python (least-squares, regularized LS, L1-regularized LS, robust regression)
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