A PyTorch-based End-to-End Predict-then-Optimize Library for Linear and Integer Programming

[arXiv] Conformal Prediction to Control False Positives in Medical Image Segmentation

[ICML'24] Conformal Prediction for Deep Classifier via Label Ranking

APDTFlow is a modern and extensible forecasting framework for time series data that leverages advanced techniques including neural ordinary differential equations (Neural ODEs), transformer-based c…

code to reproduce results in paper Residual Distribution Predictive Systems

Tries to shrink your Pandas column dtypes with no data loss so you have more spare RAM

A Python package for fitting Quinlan's Cubist regression model

Conformal Prediction for Time-series Forecasting with Change Points

The best ChatGPT that $100 can buy.

The provided files implement the method proposed in the paper "Split conformal classification with unsupervised calibration"

Official implementation for "TimeXer: Empowering Transformers for Time Series Forecasting with Exogenous Variables" (NeurIPS 2024)

Julia package for time series forecasting, inspired by R’s forecast package—part of the TAFS Forecasting Ecosystem.

Adaptive, distribution-free prediction intervals for financial time series using Temporal Conformal Prediction (TCP). Benchmarked against GARCH, Quantile Regression, and Historical Simulation.

A Python library for conformal prediction, providing tools for uncertainty quantification in regression and classification tasks.

Tsururu is a Python-based library that provides a wide range of multi-series and multi-point-ahead prediction strategies, compatible with any underlying model, including neural networks.

R package to compute distribution-free prediction bands using density estimators

R package - Dynamic Ensembles for Time Series Forecasting

This repository holds the code developed during my Master theis

[COPA 2025] Robust Vision-Based Runway Detection through Conformal Prediction and Conformal mAP

implementation of (1) conformal thresholded intervals (CTI), (2) entropy reweighted conformal classification, (3) rank-based conformal prediction (RANK), and (4) conformal inter-quantile regression…

pyFAST is a research-driven, modular Python framework built for advanced and efficient time series analysis, especially excelling in multi-source and sparse data scenarios.

Code for evaluating and implementing Conformal Prediction analysis as part of my MSc thesis project at Imperial College London.

Conformal Prediction for Reinforcement Learning