Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
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Updated
Aug 14, 2024 - Python
Lightning-UQ-Box: Uncertainty Quantification for Neural Networks with PyTorch and Lightning
Code for "Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets", Arxiv 2024.
Code for "Conformal Performance Range Prediction for Segmentation Output Quality Control" accepted to MICCAI UNSURE 2024
A Library for Uncertainty Quantification.
Implementation for our paper "Metric-guided Image Reconstruction Bounds via Conformal Prediction".
🚩 Conformal Anomaly Detection for 'PyOD' models.
Clusters protein chains based on CA distance difference
Public release for "Explore until Confident: Efficient Exploration for Embodied Question Answering"
👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.
Code for Conformal Counterfactual Inference under Hidden Confounding (KDD’24)
👖 Conformal Tights adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor or Darts forecaster
Conformal classifiers, regressors and predictive systems
Code and experiments related to the paper: 'Enhancing reliability in prediction intervals using point forecasters: Heteroscedastic Quantile Regression and Width-Adaptive Conformal Inference'
This repository implements conformal prediction methods for classification tasks that can automatically adapt to random label contamination in the calibration sample.
Code for "Designing Decision Support Systems Using Counterfactual Prediction Sets". ICML 2024.
Code for "Towards Human-AI Complementarity with Predictions Sets", arXiv 2024
Repository for the NeurIPS 2023 paper "Beyond Confidence: Reliable Models Should Also Consider Atypicality"
In this repo, I implement, compare and reproduce results for different conformal prediction methods for various graph machine learning models.
Neo LS-SVM is a modern Least-Squares Support Vector Machine implementation
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