Python package for conformal prediction
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Updated
Jul 8, 2026 - Python
Python package for conformal prediction
Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
Code for the ICML 2023 paper "Fast Online Value-Maximizing Prediction Sets with Conformal Cost Control"
PAC Prediction Sets Under Covariate Shift
Code for "Designing Decision Support Systems Using Counterfactual Prediction Sets". ICML 2024.
Code for "Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets", NeurIPS 2024.
This repo contains the codes, images, report and slides for the first project of the course - MTH673A: Robust Statistical Methods at IIT Kanpur during the academic year 2022-2023.
Full-cycle R project implementing Conformal Prediction (LAC & APS) for distribution-free uncertainty quantification on MNIST, using a neural network built from scratch in base R. Verified 89.81% empirical coverage at a 90% target across 50 Monte Carlo splits, with a mean prediction-set size of 1.03. Base R · dslabs.
Split-conformal prediction sets with a guaranteed marginal coverage — and a demo that naive softmax confidence plateaus at model accuracy and can't deliver 90/95% coverage
Conformal prediction for Tsetlin Machines: wrap any TM and get prediction sets with finite-sample coverage guarantees. Split, Mondrian, and drift-adaptive calibration, plus recourse that stays valid under noise.
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