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Ilia Shumailov

Ilia Shumailov has 2 papers in our library, in 2023–2024; often with Nicolas Papernot, Ross Anderson, Yarin Gal.

2 papers in our library · 2023–2024

OpenAlex · ORCID · Wikidata · h-index 16 · 103 works · 1,761 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 222 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #2500

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓University of OxfordOxford · United Kingdom2022–2026 · 23 paperscurrent🏢Google DeepMindLondon · United Kingdom2024–2026 · 11 paperscurrent🎓University of CambridgeCambridge · United Kingdom2017–2025 · 26 papers🔬Vector InstituteToronto · Canada2021–2023 · 11 papers🎓University of TorontoToronto · Canada2021–2025 · 4 papers🏢GoogleMountain View · United States2024–2025 · 2 papers🎓Imperial College LondonLondon · United Kingdom2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 more institutions on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 9The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workSafety Research · 1 workSocial Sciences · 1 workInformation Systems and Management · 1 workDecision Sciences · 1 workMathematics · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AI models collapse when trained on recursively generated data (2024 · 863 citations)
    The paper examines what happens to generative models once their own outputs make up much of the web data used to train their successors.
  2. The Curse of Recursion: Training on Generated Data Makes Models Forget (2023 · 255 citations)
    Generative models are increasingly filling the web with text and images, so future models will likely be trained partly on their predecessors' output.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. AI models collapse when trained on recursively generated dataNatureIlia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.2024 · 863 citations
  2. The Curse of Recursion: Training on Generated Data Makes Models ForgetarXiv:2305.17493Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.2023 · 255 citations
  3. Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research ↗neural information processing systems · no summary yetA. Feder Cooper et al.2025 · 17 citations
  4. Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy, Research, and Practice ↗SSRN Electronic Journal · no summary yetA. Feder Cooper et al.2025 · 12 citations

 Co-authors here

5 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 222 times (of the 149,439 papers whose bibliographies we read); 60 citing papers with a page here →.

Updated

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