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awesome-conformal-prediction is a curated “awesome list” repository on GitHub collecting high-quality resources related to conformal prediction: tutorials, books, papers, theses, open-source libraries, videos, and other educational material. It is not a software library itself but a directory of resources for those wanting to learn or work with conformal prediction and uncertainty quantification. This exceptional resource is the culmination of my PhD journey in Machine Learning, specializing in Conformal Prediction under the supervision of its creator, Prof. Vladimir Vovk. Since 2015, I have painstakingly gathered these invaluable resources, and upon completing my PhD (my thesis, "Machine Learning for Probabilistic Prediction," can be found in the "Theses" section), I am thrilled to share my expertise with the global machine learning community. Immerse yourself in a professionally curated collection that has been honed through years of dedication and experience.
Features
- Large curated collection of academic papers, books, MSc/PhD theses, tutorials etc related to conformal prediction
- Open-source libraries and implementations in multiple languages (R, Python, Julia etc.) referenced / linked so users can find code to try out
- Videos / lectures, courses, blog posts etc included for varied learning styles
- Regular updates to include new resources (papers, tutorials etc.) as the field develops
- Categorization / structure: separating resources by type (libraries, theory, tutorials, etc.) to help users navigate efficiently
- Licensing / citation guidance—entries often point to license of the resource, or include citation files (CITATION.cff) etc.
Project Samples
