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Luke Hewitt

Luke Hewitt has 2 papers in our library, in 2019–2020, most of them on Code & programming languages; often with Armando Solar-Lezama, Joshua B. Tenenbaum, Maxwell Nye.

2 papers in our library · 2019–2020

OpenAlex · ORCID · h-index 11 · 28 works · 554 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,178 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #9008

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

🎓Stanford UniversityStanford · United States2023–2026 · 15 paperscurrent🎓Massachusetts Institute of TechnologyCambridge · United States2018–2024 · 12 papersEducation🎓Stanford Center on Philanthropy and Civil SocietyStanford · United States2024–2025 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 1 workComputer Science Applications · 1 workInformation Systems · 1 work

🧭 Topics 1Research subjects and languages the work is about.Code & programming languages · 1 work

 Works by year

2020s

20201 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Learning to Infer Program Sketches (2019 · 53 citations)
    The paper addresses program synthesis from specifications that people find easy to give, namely input-output examples and natural-language descriptions.
  2. DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning (2020 · 22 citations)
    Program induction treats learning a task as finding a program that produces the desired behavior. It generalizes well from a few examples and yields knowledge humans can read. In practice, though, it has relied on hand-crafted domain-specific languages (DSLs) and search procedures tuned to them. DreamCoder learns…

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. Quantifying the potential persuasive returns to political microtargeting ↗Proceedings of the National Academy of Sciences · no summary yetBen M Tappin et al.2023 · 83 citations
  2. Learning to Infer Program SketchesarXiv:1902.06349Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, et al.2019 · 53 citations
  3. DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningarXiv:2006.08381Kevin Ellis, Catherine Wong, Maxwell Nye, et al.2020 · 22 citations

 Co-authors here

6 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 2,178 times (of the 149,439 papers whose bibliographies we read); 615 citing papers with a page here →.

Updated

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