This repository serves as a curated bibliography of academic papers on machine unlearning, covering research from 2000 to the present. It is intended for researchers, practitioners, and students interested in understanding and advancing the field of data deletion and model modification in machine learning.

How It Works

The repository is structured as a comprehensive list of publications, categorized by year and presented in a table format. Each entry includes author(s), title, and venue, providing a historical overview and a detailed catalog of research contributions to machine unlearning.

Highlighted Details

• Extensive coverage of machine unlearning research from 2000 to 2025.
• Includes papers from top-tier conferences (e.g., NeurIPS, ICML, ICLR, CVPR) and journals.
• Covers a wide range of sub-topics within machine unlearning, including federated unlearning, graph unlearning, and unlearning for generative models.
• Features foundational papers alongside the latest advancements in the field.

Maintenance & Community

This is a static collection of literature; there is no active development or community interaction associated with this repository.

Licensing & Compatibility

The repository itself does not contain code and is a collection of links to academic papers. Licensing information would pertain to the individual papers or their respective publishers.

Limitations & Caveats

This repository is a bibliography and does not provide any code, tools, or implementations for machine unlearning. Users must seek out the individual papers for practical application or further research.