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Jonathan Hayase

Jonathan Hayase has 2 papers in our library, in 2023, most of them on Language models & pretraining, Vision & language; often with Alex Fang, Christopher A. Choquette-Choo, Daphne Ippolito.

2 papers in our library · 2023

OpenAlex · ORCID · h-index 7 · 23 works · 273 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 248 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4616

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 WashingtonSeattle · United States2021–2026 · 13 paperscurrent🔬Allen Institute for Artificial IntelligenceSeattle · United States2024–2025 · 2 papers🎓Harvey Mudd CollegeClaremont · United States2019–2021 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksComputer Vision and Pattern Recognition · 1 workArtificial Intelligence · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Vision & language · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20232 papers

 Papers

The most cited first (OpenAlex and our library).

  1. DataComp: In search of the next generation of multimodal datasets (2023 · 224 citations)
    Large image-text corpora underpin models such as CLIP and Stable Diffusion. Yet dataset construction gets far less systematic study than architectures or training recipes, and many strong corpora are closed. DataComp addresses this by reversing the usual benchmark setup. The CLIP training code, hyperparameters and…
  2. Scalable Extraction of Training Data from (Production) Language Models (2023 · 211 citations)
    The paper asks how much training data an attacker can actually pull out of a language model without knowing what the model was trained on.

All works

3 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. DataComp: In search of the next generation of multimodal datasetsConference on Neural Information Processing Systems (NeurIPS)Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, et al.2023 · 224 citations
  2. Scalable Extraction of Training Data from (Production) Language ModelsarXiv:2311.17035Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.2023 · 211 citations
  3. Anchored Decoding: Provably Reducing Copyright Risk for Any Language Model ↗arXiv (Cornell University) · no summary yetJacqueline He et al.2026

 Co-authors here

12 of 15 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 248 times (of the 149,439 papers whose bibliographies we read); 55 citing papers with a page here →.

Updated

This page in Markdown → https://md.research.elamur.ai/free/en/authors/jonathan-hayase/