Exercises for the Deep Learning textbook at www.deeplearningbook.org

This package is a work in progress compiling functions to make it easier running placebo in time tests with synthetic control models and SCM with multiple treated units.

↙️ ↘️ An R package for working with causal directed acyclic graphs (DAGs)

Replication code and downloadable example data sets for The Effect

Lectures and conference materials for the DSE2023 at the University of Lausanne, Switzerland

Conformal prediction for time-series applications.

Practical Guide to Applied Conformal Prediction, published by Packt

Materials for STAT 991: Topics In Modern Statistical Learning (UPenn, 2022 Spring) - uncertainty quantification, conformal prediction, calibration, etc

Predictive Uncertainty Quantification through Conformal Prediction for Machine Learning models trained in MLJ.

Methods for online conformal prediction.

Multi-class probabilistic classification using inductive and cross Venn–Abers predictors

Conformal prediction for controlling monotonic risk functions. Simple accompanying PyTorch code for conformal risk control in computer vision and natural language processing.

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).

Lightweight, useful implementation of conformal prediction on real data.

Python implementation of the conformal prediction framework.