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Bilal Piot

Bilal Piot has 2 papers in our library, in 2020–2023, most of them on Evaluation & benchmarks; often with Daniel Guo, Charles Blundell, Daniele Calandriello.

2 papers in our library · 2020–2023

OpenAlex · ORCID · Wikidata · h-index 25 · 93 works · 9,364 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 2,809 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #4386

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 Kingdom2016–2024 · 22 papers🎓Université de LilleLille · France2014–2017 · 10 papers🔬Centre de Recherche en Informatique, Signal et Automatique de LilleVilleneuve-d'Ascq · France2015–2017 · 9 papers🏢GoogleMountain View · United States2017–2024 · 7 papers🔬Georgia Tech-CNRS LaboratoryMetz · France2013–2014 · 6 papers🎓SupélecGif-sur-Yvette · France2013–2014 · 6 papers🔬Centre National de la Recherche ScientifiqueParis · France2013–2016 · 5 papers🔬Centre Inria de l'Université de LilleVilleneuve-d'Ascq · France2014–2016 · 3 papers🎓Université Lille 1Lille · France2016 · 2 papers🎓Georgia Tech-EuropeMetz · France2014 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksDecision Sciences · 1 workManagement Science and Operations Research · 1 work

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1 work

 Works by year

2020s

20231 paper20201 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A General Theoretical Paradigm to Understand Learning from Human Preferences (2023 · 263 citations)
    This paper by researchers at Google DeepMind provides a theoretical foundation for methods that train language models from human preferences.
  2. Agent57: Outperforming the Atari Human Benchmark (2020 · 235 citations)
    The suite of 57 Atari 2600 games (the ALE environment) has served for about ten years as the standard test of how general reinforcement learning (RL) algorithms are.

All works

6 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. Bootstrap your own latent: A new approach to self-supervised Learning ↗arXiv (Cornell University) · no summary yetJean-Bastien Grill et al.2020 · 3,531 citations
  2. Gemma 2: Improving Open Language Models at a Practical Size ↗arXiv:2408.00118 · no summary yetGemma Team et al.2024 · 1,302 citations
  3. Gemma 3 Technical Report ↗arXiv:2503.19786 · no summary yetGemma Team et al.2025 · 1,147 citations
  4. Noisy Networks for Exploration ↗arXiv (Cornell University) · no summary yetMeire Fortunato et al.2017 · 570 citations
  5. A General Theoretical Paradigm to Understand Learning from Human PreferencesarXiv:2310.12036Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, et al.2023 · 263 citations
  6. Agent57: Outperforming the Atari Human BenchmarkarXiv:2003.13350Adrià Puigdomènech Badia, Bilal Piot, Steven Kapturowski, et al.2020 · 235 citations

 Co-authors here

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

 Cited by

Papers of our library cite their works 2,809 times (of the 149,439 papers whose bibliographies we read); 666 citing papers with a page here →.

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