🧑‍🔬 Michal Valko

🏛️ Affiliations

🏢Google DeepMindLondon · United Kingdom2018–2026 · 60 paperscurrent🔬Institut national de recherche en sciences et technologies du numériqueLe Chesnay-Rocquencourt · France2011–2026 · 48 paperscurrent🔬Centre Inria de l'Université de LilleVilleneuve-d'Ascq · France2011–2026 · 25 paperscurrent🎓University of PittsburghPittsburgh · United States2006–2026 · 15 paperscurrent🎓Catholic University in RuzomberokRužomberok · Slovakia2022–2026 · 4 papersEducation 2022–2025 · Bachelor, Theological institute in Spišské PodhradieEducation since 2025 · Master, Theological Institutecurrent🏢SequellaRockville · United States2026 · 3 paperscurrent🏢IntelSanta Clara · United States2010–2026 · 2 paperscurrent🎓Université de LilleLille · France2016–2026 · 2 paperscurrent🎓University of CambridgeCambridge · United Kingdom2026 · 1 papercurrent🔬École Normale Supérieure - PSLParis · France2021–2025 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 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.

📅 Works by year

2020s

📄 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. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem Solving (2024 · 45 citations)
   The authors ask whether large language models have something like metacognitive knowledge, meaning an awareness of which skills a task calls for, and whether that awareness can be used to improve their performance.
3. Nash Learning from Human Feedback (2023 · 32 citations)
   Standard RLHF reduces pairwise human judgments to a scalar reward, typically through a Bradley-Terry model, and then optimizes the LLM against that score.

All works

5 works of theirs in our library, the most cited first; 3 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. A General Theoretical Paradigm to Understand Learning from Human PreferencesarXiv:2310.12036Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, et al.2023 · 263 citations
3. Broaden Your Views for Self-Supervised Video Learning ↗IEEE/CVF International Conference on Computer Vision (ICCV) · no summary yetAdrià Recasens et al.2021 · 97 citations
4. Metacognitive Capabilities of LLMs: An Exploration in Mathematical Problem SolvingarXiv:2405.12205Aniket Didolkar, Anirudh Goyal, Nan Rosemary Ke, et al.2024 · 45 citations
5. Nash Learning from Human FeedbackarXiv:2312.00886Remi Munos, Michal Valko, Daniele Calandriello, et al.2023 · 32 citations

🤝 Co-authors here

12 of 18 co-authors with a page, the most papers together first.

• Daniele Calandriello ×2
• Mark Rowland ×2
• Mohammad Gheshlaghi Azar ×2
• Rémi Munos ×2
• Andrea Michi
• Aniket Didolkar
• Anirudh Goyal
• Bilal Piot
• Daniel Guo
• Danilo Jimenez Rezende
• Matthieu Geist
• Michael C. Mozer

Venues

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

• IEEE/CVF International Conference on Computer Vision (ICCV)

🔗 Cited by

Papers of our library cite their works 581 times (of the 149,439 papers whose bibliographies we read); 104 citing papers with a page here →.

Updated 3 November 2026

↑ back to top

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