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Robert Stanforth

Robert Stanforth has 2 papers in our library, in 2019, most of them on Language models & pretraining, Sentiment & emotion, Evaluation & benchmarks; often with Dani Yogatama, Johannes Welbl, Po-Sen Huang.

2 papers in our library · 2019

OpenAlex · h-index 17 · 31 works · 1,788 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 142 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #5819

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

🏢Google DeepMindLondon · United Kingdom2018–2024 · 9 paperscurrent🏢GoogleMountain View · United States2019–2024 · 4 paperscurrent🎓University College LondonLondon · United Kingdom2019–2020 · 4 papers🎓Massachusetts Institute of TechnologyCambridge · United States2020–2021 · 3 papers🎓Birkbeck, University of LondonLondon · United Kingdom2007 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 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 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksComputational Theory and Mathematics · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 workSentiment & emotion · 1 work

 Works by year

2010s

20192 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Achieving Verified Robustness to Symbol Substitutions via Interval Bound Propagation (2019 · 163 citations)
    Neural text classifiers can be fooled by small edits such as swapping a word for a synonym or flipping characters.
  2. Reducing Sentiment Bias in Language Models via Counterfactual Evaluation (2019 · 104 citations)
    The paper examines whether text produced by large language models carries sentiment bias, where the tone of a continuation shifts depending on which country, occupation or person name appears in the prompt.

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. Achieving Verified Robustness to Symbol Substitutions via Interval Bound PropagationConference on Empirical Methods in Natural Language Processing (EMNLP)Po-Sen Huang, Robert Stanforth, Johannes Welbl, et al.2019 · 163 citations
  2. Scalable Verified Training for Provably Robust Image Classification ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetSven Gowal et al.2019 · 149 citations
  3. Reducing Sentiment Bias in Language Models via Counterfactual EvaluationarXiv:1911.03064Po-Sen Huang, Huan Zhang, Ray Jiang, et al.2019 · 104 citations

 Co-authors here

9 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 142 times (of the 149,439 papers whose bibliographies we read); 28 citing papers with a page here →.

Updated

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