# Credit Attribution and Stable Compression

arXiv: https://arxiv.org/abs/2406.15916 · HTML: https://mlsanity.com/paper/2406.15916/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 22 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [On Provable Copyright Protection for Generative Models](https://mlsanity.com/paper/2302.10870/) (2023)
- [Private PAC learning implies finite Littlestone dimension](https://mlsanity.com/paper/1806.00949/) (2018)
- [An Equivalence Between Private Classification and Online Prediction](https://mlsanity.com/paper/2003.00563/) (2020)
- [Private Learning and Sanitization: Pure vs. Approximate Differential Privacy](https://mlsanity.com/paper/1407.2674/) (2014)
- [Differentially Private Release and Learning of Threshold Functions](https://mlsanity.com/paper/1504.07553/) (2015)
- [Proper Learning, Helly Number, and an Optimal SVM Bound](https://mlsanity.com/paper/2005.11818/) (2020)
- [Limits of Private Learning with Access to Public Data](https://mlsanity.com/paper/1910.11519/) (2019)
- [Leveraging Public Data for Practical Private Query Release](https://mlsanity.com/paper/2102.08598/) (2021)
- [Private Estimation with Public Data](https://mlsanity.com/paper/2208.07984/) (2022)
- [Private Query Release Assisted by Public Data](https://mlsanity.com/paper/2004.10941/) (2020)
- [Can Copyright be Reduced to Privacy?](https://mlsanity.com/paper/2305.14822/) (2023)
- [Formalizing Human Ingenuity: A Quantitative Framework for Copyright Law's Substantial Similarity](https://mlsanity.com/paper/2206.01230/) (2022)
- [Private Distribution Learning with Public Data: The View from Sample Compression](https://mlsanity.com/paper/2308.06239/) (2023)
- [PILLAR: How to make semi-private learning more effective](https://mlsanity.com/paper/2306.03962/) (2023)
- [Oracle-Efficient Differentially Private Learning with Public Data](https://mlsanity.com/paper/2402.09483/) (2024)
- [Boosting, Voting Classifiers and Randomized Sample Compression Schemes](https://mlsanity.com/paper/2402.02976/) (2024)

## Built on by (most cited first, 3 of 3)

- [Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models](https://mlsanity.com/paper/2506.19881/) (2025)
- [Reconstructing Template-Memorized Images from Natural Prompts](https://mlsanity.com/paper/2507.07947/) (2025)
- [Barriers to Counterfactual Credit Attribution for Autoregressive Models](https://mlsanity.com/paper/2605.01425/) (2026)

## Abstract (arXiv)

Credit attribution is crucial across various fields. In academic research, proper citation acknowledges prior work and establishes original contributions. Similarly, in generative models, such as those trained on existing artworks or music, it is important to ensure that any generated content influenced by these works appropriately credits the original creators. We study credit attribution by machine learning algorithms. We propose new definitions--relaxations of Differential Privacy--that weaken the stability guarantees for a designated subset of k datapoints. These k datapoints can be used non-stably with permission from their owners, potentially in exchange for compensation. Meanwhile, the remaining datapoints are guaranteed to have no significant influence on the algorithm's output. Our framework extends well-studied notions of stability, including Differential Privacy (k = 0), differentially private learning with public data (where the k public datapoints are fixed in advance), and stable sample compression (where the k datapoints are selected adaptively by the algorithm). We examine the expressive power of these stability notions within the PAC learning framework, provide a comprehensive characterization of learnability for algorithms adhering to these principles, and propose directions and questions for future research.